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Unformatted text preview: 8 Cluster Analysis: Basic Concepts and Algorithms Cluster analysis divides data into groups (clusters) that are meaningful, useful, or both. If meaningful groups are the goal, then the clusters should capture the natural structure of the data. In some cases, however, cluster analysis is only a useful starting point for other purposes, such as data summarization. Whether for understanding or utility, cluster analysis has long played an important role in a wide variety of ﬁelds: psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. There have been many applications of cluster analysis to practical problems. We provide some speciﬁc examples, organized by whether the purpose of the clustering is understanding or utility. Clustering for Understanding Classes, or conceptually meaningful groups of objects that share common characteristics, play an important role in how people analyze and describe the world. Indeed, human beings are skilled at dividing objects into groups (clustering) and assigning particular objects to these groups (classiﬁcation). For example, even relatively young children can quickly label the objects in a photograph as buildings, vehicles, people, animals, plants, etc. In the context of understanding data, clusters are potential classes and cluster analysis is the study of techniques for automatically ﬁnding classes. The following are some examples: 488 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms • Biology. Biologists have spent many years creating a taxonomy (hierarchical classiﬁcation) of all living things: kingdom, phylum, class, order, family, genus, and species. Thus, it is perhaps not surprising that much of the early work in cluster analysis sought to create a discipline of mathematical taxonomy that could automatically ﬁnd such classiﬁcation structures. More recently, biologists have applied clustering to analyze the large amounts of genetic information that are now available. For example, clustering has been used to ﬁnd groups of genes that have similar functions. • Information Retrieval. The World Wide Web consists of billions of Web pages, and the results of a query to a search engine can return thousands of pages. Clustering can be used to group these search results into a small number of clusters, each of which captures a particular aspect of the query. For instance, a query of “movie” might return Web pages grouped into categories such as reviews, trailers, stars, and theaters. Each category (cluster) can be broken into subcategories (subclusters), producing a hierarchical structure that further assists a user’s exploration of the query results. • Climate. Understanding the Earth’s climate requires ﬁnding patterns in the atmosphere and ocean. To that end, cluster analysis has been applied to ﬁnd patterns in the atmospheric pressure of polar regions and areas of the ocean that have a signiﬁcant impact on land climate. • Psychology and Medicine. An illness or condition frequently has a number of variations, and cluster analysis can be used to identify these diﬀerent subcategories. For example, clustering has been used to identify diﬀerent types of depression. Cluster analysis can also be used to detect patterns in the spatial or temporal distribution of a disease. • Business. Businesses collect large amounts of information on current and potential customers. Clustering can be used to segment customers into a small number of groups for additional analysis and marketing activities. Clustering for Utility Cluster analysis provides an abstraction from individual data objects to the clusters in which those data objects reside. Additionally, some clustering techniques characterize each cluster in terms of a cluster prototype; i.e., a data object that is representative of the other objects in the cluster. These cluster prototypes can be used as the basis for a 489 number of data analysis or data processing techniques. Therefore, in the context of utility, cluster analysis is the study of techniques for ﬁnding the most representative cluster prototypes. • Summarization. Many data analysis techniques, such as regression or PCA, have a time or space complexity of O(m2 ) or higher (where m is the number of objects), and thus, are not practical for large data sets. However, instead of applying the algorithm to the entire data set, it can be applied to a reduced data set consisting only of cluster prototypes. Depending on the type of analysis, the number of prototypes, and the accuracy with which the prototypes represent the data, the results can be comparable to those that would have been obtained if all the data could have been used. • Compression. Cluster prototypes can also be used for data compression. In particular, a table is created that consists of the prototypes for each cluster; i.e., each prototype is assigned an integer value that is its position (index) in the table. Each object is represented by the index of the prototype associated with its cluster. This type of compression is known as vector quantization and is often applied to image, sound, and video data, where (1) many of the data objects are highly similar to one another, (2) some loss of information is acceptable, and (3) a substantial reduction in the data size is desired. • Eﬃciently Finding Nearest Neighbors. Finding nearest neighbors can require computing the pairwise distance between all points. Often clusters and their cluster prototypes can be found much more eﬃciently. If objects are relatively close to the prototype of their cluster, then we can use the prototypes to reduce the number of distance computations that are necessary to ﬁnd the nearest neighbors of an object. Intuitively, if two cluster prototypes are far apart, then the objects in the corresponding clusters cannot be nearest neighbors of each other. Consequently, to ﬁnd an object’s nearest neighbors it is only necessary to compute the distance to objects in nearby clusters, where the nearness of two clusters is measured by the distance between their prototypes. This idea is made more precise in Exercise 25 on page 94. This chapter provides an introduction to cluster analysis. We begin with a high-level overview of clustering, including a discussion of the various approaches to dividing objects into sets of clusters and the diﬀerent types of clusters. We then describe three speciﬁc clustering techniques that represent 490 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms broad categories of algorithms and illustrate a variety of concepts: K-means, agglomerative hierarchical clustering, and DBSCAN. The ﬁnal section of this chapter is devoted to cluster validity—methods for evaluating the goodness of the clusters produced by a clustering algorithm. More advanced clustering concepts and algorithms will be discussed in Chapter 9. Whenever possible, we discuss the strengths and weaknesses of diﬀerent schemes. In addition, the bibliographic notes provide references to relevant books and papers that explore cluster analysis in greater depth. 8.1 Overview Before discussing speciﬁc clustering techniques, we provide some necessary background. First, we further deﬁne cluster analysis, illustrating why it is diﬃcult and explaining its relationship to other techniques that group data. Then we explore two important topics: (1) diﬀerent ways to group a set of objects into a set of clusters, and (2) types of clusters. 8.1.1 What Is Cluster Analysis? Cluster analysis groups data objects based only on information found in the data that describes the objects and their relationships. The goal is that the objects within a group be similar (or related) to one another and diﬀerent from (or unrelated to) the objects in other groups. The greater the similarity (or homogeneity) within a group and the greater the diﬀerence between groups, the better or more distinct the clustering. In many applications, the notion of a cluster is not well deﬁned. To better understand the diﬃculty of deciding what constitutes a cluster, consider Figure 8.1, which shows twenty points and three diﬀerent ways of dividing them into clusters. The shapes of the markers indicate cluster membership. Figures 8.1(b) and 8.1(d) divide the data into two and six parts, respectively. However, the apparent division of each of the two larger clusters into three subclusters may simply be an artifact of the human visual system. Also, it may not be unreasonable to say that the points form four clusters, as shown in Figure 8.1(c). This ﬁgure illustrates that the deﬁnition of a cluster is imprecise and that the best deﬁnition depends on the nature of data and the desired results. Cluster analysis is related to other techniques that are used to divide data objects into groups. For instance, clustering can be regarded as a form of classiﬁcation in that it creates a labeling of objects with class (cluster) labels. However, it derives these labels only from the data. In contrast, classiﬁcation 8.1 (a) Original points. (c) Four clusters. Overview 491 (b) Two clusters. (d) Six clusters. Figure 8.1. Different ways of clustering the same set of points. in the sense of Chapter 4 is supervised classiﬁcation; i.e., new, unlabeled objects are assigned a class label using a model developed from objects with known class labels. For this reason, cluster analysis is sometimes referred to as unsupervised classiﬁcation. When the term classiﬁcation is used without any qualiﬁcation within data mining, it typically refers to supervised classiﬁcation. Also, while the terms segmentation and partitioning are sometimes used as synonyms for clustering, these terms are frequently used for approaches outside the traditional bounds of cluster analysis. For example, the term partitioning is often used in connection with techniques that divide graphs into subgraphs and that are not strongly connected to clustering. Segmentation often refers to the division of data into groups using simple techniques; e.g., an image can be split into segments based only on pixel intensity and color, or people can be divided into groups based on their income. Nonetheless, some work in graph partitioning and in image and market segmentation is related to cluster analysis. 8.1.2 Diﬀerent Types of Clusterings An entire collection of clusters is commonly referred to as a clustering, and in this section, we distinguish various types of clusterings: hierarchical (nested) versus partitional (unnested), exclusive versus overlapping versus fuzzy, and complete versus partial. Hierarchical versus Partitional The most commonly discussed distinction among diﬀerent types of clusterings is whether the set of clusters is nested 492 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms or unnested, or in more traditional terminology, hierarchical or partitional. A partitional clustering is simply a division of the set of data objects into non-overlapping subsets (clusters) such that each data object is in exactly one subset. Taken individually, each collection of clusters in Figures 8.1 (b–d) is a partitional clustering. If we permit clusters to have subclusters, then we obtain a hierarchical clustering, which is a set of nested clusters that are organized as a tree. Each node (cluster) in the tree (except for the leaf nodes) is the union of its children (subclusters), and the root of the tree is the cluster containing all the objects. Often, but not always, the leaves of the tree are singleton clusters of individual data objects. If we allow clusters to be nested, then one interpretation of Figure 8.1(a) is that it has two subclusters (Figure 8.1(b)), each of which, in turn, has three subclusters (Figure 8.1(d)). The clusters shown in Figures 8.1 (a–d), when taken in that order, also form a hierarchical (nested) clustering with, respectively, 1, 2, 4, and 6 clusters on each level. Finally, note that a hierarchical clustering can be viewed as a sequence of partitional clusterings and a partitional clustering can be obtained by taking any member of that sequence; i.e., by cutting the hierarchical tree at a particular level. Exclusive versus Overlapping versus Fuzzy The clusterings shown in Figure 8.1 are all exclusive, as they assign each object to a single cluster. There are many situations in which a point could reasonably be placed in more than one cluster, and these situations are better addressed by non-exclusive clustering. In the most general sense, an overlapping or non-exclusive clustering is used to reﬂect the fact that an object can simultaneously belong to more than one group (class). For instance, a person at a university can be both an enrolled student and an employee of the university. A non-exclusive clustering is also often used when, for example, an object is “between” two or more clusters and could reasonably be assigned to any of these clusters. Imagine a point halfway between two of the clusters of Figure 8.1. Rather than make a somewhat arbitrary assignment of the object to a single cluster, it is placed in all of the “equally good” clusters. In a fuzzy clustering, every object belongs to every cluster with a membership weight that is between 0 (absolutely doesn’t belong) and 1 (absolutely belongs). In other words, clusters are treated as fuzzy sets. (Mathematically, a fuzzy set is one in which an object belongs to any set with a weight that is between 0 and 1. In fuzzy clustering, we often impose the additional constraint that the sum of the weights for each object must equal 1.) Similarly, probabilistic clustering techniques compute the probability with which each 8.1 Overview 493 point belongs to each cluster, and these probabilities must also sum to 1. Because the membership weights or probabilities for any object sum to 1, a fuzzy or probabilistic clustering does not address true multiclass situations, such as the case of a student employee, where an object belongs to multiple classes. Instead, these approaches are most appropriate for avoiding the arbitrariness of assigning an object to only one cluster when it may be close to several. In practice, a fuzzy or probabilistic clustering is often converted to an exclusive clustering by assigning each object to the cluster in which its membership weight or probability is highest. Complete versus Partial A complete clustering assigns every object to a cluster, whereas a partial clustering does not. The motivation for a partial clustering is that some objects in a data set may not belong to well-deﬁned groups. Many times objects in the data set may represent noise, outliers, or “uninteresting background.” For example, some newspaper stories may share a common theme, such as global warming, while other stories are more generic or one-of-a-kind. Thus, to ﬁnd the important topics in last month’s stories, we may want to search only for clusters of documents that are tightly related by a common theme. In other cases, a complete clustering of the objects is desired. For example, an application that uses clustering to organize documents for browsing needs to guarantee that all documents can be browsed. 8.1.3 Diﬀerent Types of Clusters Clustering aims to ﬁnd useful groups of objects (clusters), where usefulness is deﬁned by the goals of the data analysis. Not surprisingly, there are several diﬀerent notions of a cluster that prove useful in practice. In order to visually illustrate the diﬀerences among these types of clusters, we use two-dimensional points, as shown in Figure 8.2, as our data objects. We stress, however, that the types of clusters described here are equally valid for other kinds of data. Well-Separated A cluster is a set of objects in which each object is closer (or more similar) to every other object in the cluster than to any object not in the cluster. Sometimes a threshold is used to specify that all the objects in a cluster must be suﬃciently close (or similar) to one another. This idealistic deﬁnition of a cluster is satisﬁed only when the data contains natural clusters that are quite far from each other. Figure 8.2(a) gives an example of wellseparated clusters that consists of two groups of points in a two-dimensional space. The distance between any two points in diﬀerent groups is larger than 494 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms the distance between any two points within a group. Well-separated clusters do not need to be globular, but can have any shape. Prototype-Based A cluster is a set of objects in which each object is closer (more similar) to the prototype that deﬁnes the cluster than to the prototype of any other cluster. For data with continuous attributes, the prototype of a cluster is often a centroid, i.e., the average (mean) of all the points in the cluster. When a centroid is not meaningful, such as when the data has categorical attributes, the prototype is often a medoid, i.e., the most representative point of a cluster. For many types of data, the prototype can be regarded as the most central point, and in such instances, we commonly refer to prototypebased clusters as center-based clusters. Not surprisingly, such clusters tend to be globular. Figure 8.2(b) shows an example of center-based clusters. Graph-Based If the data is represented as a graph, where the nodes are objects and the links represent connections among objects (see Section 2.1.2), then a cluster can be deﬁned as a connected component; i.e., a group of objects that are connected to one another, but that have no connection to objects outside the group. An important example of graph-based clusters are contiguity-based clusters, where two objects are connected only if they are within a speciﬁed distance of each other. This implies that each object in a contiguity-based cluster is closer to some other object in the cluster than to any point in a diﬀerent cluster. Figure 8.2(c) shows an example of such clusters for two-dimensional points. This deﬁnition of a cluster is useful when clusters are irregular or intertwined, but can have trouble when noise is present since, as illustrated by the two spherical clusters of Figure 8.2(c), a small bridge of points can merge two distinct clusters. Other types of graph-based clusters are also possible. One such approach (Section 8.3.2) deﬁnes a cluster as a clique; i.e., a set of nodes in a graph that are completely connected to each other. Speciﬁcally, if we add connections between objects in the order of their distance from one another, a cluster is formed when a set of objects forms a clique. Like prototype-based clusters, such clusters tend to be globular. Density-Based A cluster is a dense region of objects that is surrounded by a region of low density. Figure 8.2(d) shows some density-based clusters for data created by adding noise to the data of Figure 8.2(c). The two circular clusters are not merged, as in Figure 8.2(c), because the bridge between them fades into the noise. Likewise, the curve that is present in Figure 8.2(c) also 8.1 Overview 495 fades into the noise and does not form a cluster in Figure 8.2(d). A densitybased deﬁnition of a cluster is often employed when the clusters are irregular or intertwined, and when noise and outliers are present. By contrast, a contiguitybased deﬁnition of a cluster would not work well for the data of Figure 8.2(d) since the noise would tend to form bridges between clusters. Shared-Property (Conceptual Clusters) More generally, we can deﬁne a cluster as a set of objects that share some property. This deﬁnition encompasses all the previous deﬁnitions of a cluster; e.g., objects in a center-based cluster share the property that they are all closest to the same centroid or medoid. However, the shared-property approach also includes new types of clusters. Consider the clusters shown in Figure 8.2(e). A triangular area (cluster) is adjacent to a rectangular one, and there are two intertwined circles (clusters). In both cases, a clustering algorithm would need a very speciﬁc concept of a cluster to successfully detect these clusters. The process of ﬁnding such clusters is called conceptual clustering. However, too sophisticated a notion of a cluster would take us into the area of pattern recognition, and thus, we only consider simpler types of clusters in this book. Road Map In this chapter, we use the following three simple, but important techniques to introduce many of the concepts involved in cluster analysis. • K-means. This is a prototype-based, partitional clustering technique that attempts to ﬁnd a user-speciﬁed number of clusters (K ), which are represented by their centroids. • Agglomerative Hierarchical Clustering. This clustering approach refers to a collection of closely related clustering techniques that produce a hierarchical clustering by starting with each point as a singleton cluster and then repeatedly merging the two closest clusters until a single, allencompassing cluster remains. Some of these techniques have a natural interpretation in terms of graph-based clustering, while others have an interpretation in terms of a prototype-based approach. • DBSCAN. This is a density-based clustering algorithm that produces a partitional clustering, in which the number of clusters is automatically determined by the algorithm. Points in low-density regions are classiﬁed as noise and omitted; thus, DBSCAN does not produce a complete clustering. 496 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms (a) Well-separated clusters. Each point is closer to all of the points in its cluster than to any point in another cluster. (b) Center-based clusters. Each point is closer to the center of its cluster than to the center of any other cluster. (c) Contiguity-based clusters. Each point is closer to at least one point in its cluster than to any point in another cluster. (d) Density-based clusters. Clusters are regions of high density separated by regions of low density. (e) Conceptual clusters. Points in a cluster share some general property that derives from the entire set of points. (Points in the intersection of the circles belong to both.) Figure 8.2. Different types of clusters as illustrated by sets of two-dimensional points. 8.2 K-means Prototype-based clustering techniques create a one-level partitioning of the data objects. There are a number of such techniques, but two of the most prominent are K-means and K-medoid. K-means deﬁnes a prototype in terms of a centroid, which is usually the mean of a group of points, and is typically 8.2 K-means 497 applied to objects in a continuous n-dimensional space. K-medoid deﬁnes a prototype in terms of a medoid, which is the most representative point for a group of points, and can be applied to a wide range of data since it requires only a proximity measure for a pair of objects. While a centroid almost never corresponds to an actual data point, a medoid, by its deﬁnition, must be an actual data point. In this section, we will focus solely on K-means, which is one of the oldest and most widely used clustering algorithms. 8.2.1 The Basic K-means Algorithm The K-means clustering technique is simple, and we begin with a description of the basic algorithm. We ﬁrst choose K initial centroids, where K is a userspeciﬁed parameter, namely, the number of clusters desired. Each point is then assigned to the closest centroid, and each collection of points assigned to a centroid is a cluster. The centroid of each cluster is then updated based on the points assigned to the cluster. We repeat the assignment and update steps until no point changes clusters, or equivalently, until the centroids remain the same. K-means is formally described by Algorithm 8.1. The operation of K-means is illustrated in Figure 8.3, which shows how, starting from three centroids, the ﬁnal clusters are found in four assignment-update steps. In these and other ﬁgures displaying K-means clustering, each subﬁgure shows (1) the centroids at the start of the iteration and (2) the assignment of the points to those centroids. The centroids are indicated by the “+” symbol; all points belonging to the same cluster have the same marker shape. Algorithm 8.1 Basic K-means algorithm. 1: Select K points as initial centroids. 2: repeat 3: Form K clusters by assigning each point to its closest centroid. 4: Recompute the centroid of each cluster. 5: until Centroids do not change. In the ﬁrst step, shown in Figure 8.3(a), points are assigned to the initial centroids, which are all in the larger group of points. For this example, we use the mean as the centroid. After points are assigned to a centroid, the centroid is then updated. Again, the ﬁgure for each step shows the centroid at the beginning of the step and the assignment of points to those centroids. In the second step, points are assigned to the updated centroids, and the centroids 498 Chapter 8 (a) Iteration 1. Cluster Analysis: Basic Concepts and Algorithms (b) Iteration 2. (c) Iteration 3. (d) Iteration 4. Figure 8.3. Using the K-means algorithm to ﬁnd three clusters in sample data. are updated again. In steps 2, 3, and 4, which are shown in Figures 8.3 (b), (c), and (d), respectively, two of the centroids move to the two small groups of points at the bottom of the ﬁgures. When the K-means algorithm terminates in Figure 8.3(d), because no more changes occur, the centroids have identiﬁed the natural groupings of points. For some combinations of proximity functions and types of centroids, Kmeans always converges to a solution; i.e., K-means reaches a state in which no points are shifting from one cluster to another, and hence, the centroids don’t change. Because most of the convergence occurs in the early steps, however, the condition on line 5 of Algorithm 8.1 is often replaced by a weaker condition, e.g., repeat until only 1% of the points change clusters. We consider each of the steps in the basic K-means algorithm in more detail and then provide an analysis of the algorithm’s space and time complexity. Assigning Points to the Closest Centroid To assign a point to the closest centroid, we need a proximity measure that quantiﬁes the notion of “closest” for the speciﬁc data under consideration. Euclidean (L2 ) distance is often used for data points in Euclidean space, while cosine similarity is more appropriate for documents. However, there may be several types of proximity measures that are appropriate for a given type of data. For example, Manhattan (L1 ) distance can be used for Euclidean data, while the Jaccard measure is often employed for documents. Usually, the similarity measures used for K-means are relatively simple since the algorithm repeatedly calculates the similarity of each point to each centroid. In some cases, however, such as when the data is in low-dimensional 8.2 Symbol x Ci ci c mi m K K-means 499 Table 8.1. Table of notation. Description An object. The ith cluster. The centroid of cluster Ci . The centroid of all points. The number of objects in the ith cluster. The number of objects in the data set. The number of clusters. Euclidean space, it is possible to avoid computing many of the similarities, thus signiﬁcantly speeding up the K-means algorithm. Bisecting K-means (described in Section 8.2.3) is another approach that speeds up K-means by reducing the number of similarities computed. Centroids and Objective Functions Step 4 of the K-means algorithm was stated rather generally as “recompute the centroid of each cluster,” since the centroid can vary, depending on the proximity measure for the data and the goal of the clustering. The goal of the clustering is typically expressed by an objective function that depends on the proximities of the points to one another or to the cluster centroids; e.g., minimize the squared distance of each point to its closest centroid. We illustrate this with two examples. However, the key point is this: once we have speciﬁed a proximity measure and an objective function, the centroid that we should choose can often be determined mathematically. We provide mathematical details in Section 8.2.6, and provide a non-mathematical discussion of this observation here. Data in Euclidean Space Consider data whose proximity measure is Euclidean distance. For our objective function, which measures the quality of a clustering, we use the sum of the squared error (SSE), which is also known as scatter. In other words, we calculate the error of each data point, i.e., its Euclidean distance to the closest centroid, and then compute the total sum of the squared errors. Given two diﬀerent sets of clusters that are produced by two diﬀerent runs of K-means, we prefer the one with the smallest squared error since this means that the prototypes (centroids) of this clustering are a better representation of the points in their cluster. Using the notation in Table 8.1, the SSE is formally deﬁned as follows: 500 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms K dist(ci , x)2 SSE = (8.1) i=1 x∈Ci where dist is the standard Euclidean (L2 ) distance between two objects in Euclidean space. Given these assumptions, it can be shown (see Section 8.2.6) that the centroid that minimizes the SSE of the cluster is the mean. Using the notation in Table 8.1, the centroid (mean) of the ith cluster is deﬁned by Equation 8.2. ci = 1 mi x (8.2) x∈Ci To illustrate, the centroid of a cluster containing the three two-dimensional points, (1,1), (2,3), and (6,2), is ((1 + 2 + 6)/3, ((1 + 3 + 2)/3) = (3, 2). Steps 3 and 4 of the K-means algorithm directly attempt to minimize the SSE (or more generally, the objective function). Step 3 forms clusters by assigning points to their nearest centroid, which minimizes the SSE for the given set of centroids. Step 4 recomputes the centroids so as to further minimize the SSE. However, the actions of K-means in Steps 3 and 4 are only guaranteed to ﬁnd a local minimum with respect to the SSE since they are based on optimizing the SSE for speciﬁc choices of the centroids and clusters, rather than for all possible choices. We will later see an example in which this leads to a suboptimal clustering. Document Data To illustrate that K-means is not restricted to data in Euclidean space, we consider document data and the cosine similarity measure. Here we assume that the document data is represented as a document-term matrix as described on page 31. Our objective is to maximize the similarity of the documents in a cluster to the cluster centroid; this quantity is known as the cohesion of the cluster. For this objective it can be shown that the cluster centroid is, as for Euclidean data, the mean. The analogous quantity to the total SSE is the total cohesion, which is given by Equation 8.3. K Total Cohesion = cosine(x, ci ) (8.3) i=1 x∈Ci The General Case There are a number of choices for the proximity function, centroid, and objective function that can be used in the basic K-means 8.2 K-means 501 Table 8.2. K-means: Common choices for proximity, centroids, and objective functions. Proximity Function Manhattan (L1 ) Centroid median Squared Euclidean (L2 ) 2 mean cosine mean Bregman divergence mean Objective Function Minimize sum of the L1 distance of an object to its cluster centroid Minimize sum of the squared L2 distance of an object to its cluster centroid Maximize sum of the cosine similarity of an object to its cluster centroid Minimize sum of the Bregman divergence of an object to its cluster centroid algorithm and that are guaranteed to converge. Table 8.2 shows some possible choices, including the two that we have just discussed. Notice that for Manhattan (L1 ) distance and the objective of minimizing the sum of the distances, the appropriate centroid is the median of the points in a cluster. The last entry in the table, Bregman divergence (Section 2.4.5), is actually a class of proximity measures that includes the squared Euclidean distance, L2 , 2 the Mahalanobis distance, and cosine similarity. The importance of Bregman divergence functions is that any such function can be used as the basis of a Kmeans style clustering algorithm with the mean as the centroid. Speciﬁcally, if we use a Bregman divergence as our proximity function, then the resulting clustering algorithm has the usual properties of K-means with respect to convergence, local minima, etc. Furthermore, the properties of such a clustering algorithm can be developed for all possible Bregman divergences. Indeed, K-means algorithms that use cosine similarity or squared Euclidean distance are particular instances of a general clustering algorithm based on Bregman divergences. For the rest our K-means discussion, we use two-dimensional data since it is easy to explain K-means and its properties for this type of data. But, as suggested by the last few paragraphs, K-means is a very general clustering algorithm and can be used with a wide variety of data types, such as documents and time series. Choosing Initial Centroids When random initialization of centroids is used, diﬀerent runs of K-means typically produce diﬀerent total SSEs. We illustrate this with the set of twodimensional points shown in Figure 8.3, which has three natural clusters of points. Figure 8.4(a) shows a clustering solution that is the global minimum of 502 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms (a) Optimal clustering. (b) Suboptimal clustering. Figure 8.4. Three optimal and non-optimal clusters. the SSE for three clusters, while Figure 8.4(b) shows a suboptimal clustering that is only a local minimum. Choosing the proper initial centroids is the key step of the basic K-means procedure. A common approach is to choose the initial centroids randomly, but the resulting clusters are often poor. Example 8.1 (Poor Initial Centroids). Randomly selected initial centroids may be poor. We provide an example of this using the same data set used in Figures 8.3 and 8.4. Figures 8.3 and 8.5 show the clusters that result from two particular choices of initial centroids. (For both ﬁgures, the positions of the cluster centroids in the various iterations are indicated by crosses.) In Figure 8.3, even though all the initial centroids are from one natural cluster, the minimum SSE clustering is still found. In Figure 8.5, however, even though the initial centroids seem to be better distributed, we obtain a suboptimal clustering, with higher squared error. Example 8.2 (Limits of Random Initialization). One technique that is commonly used to address the problem of choosing initial centroids is to perform multiple runs, each with a diﬀerent set of randomly chosen initial centroids, and then select the set of clusters with the minimum SSE. While simple, this strategy may not work very well, depending on the data set and the number of clusters sought. We demonstrate this using the sample data set shown in Figure 8.6(a). The data consists of two pairs of clusters, where the clusters in each (top-bottom) pair are closer to each other than to the clusters in the other pair. Figure 8.6 (b–d) shows that if we start with two initial centroids per pair of clusters, then even when both centroids are in a single 8.2 (a) Iteration 1. (b) Iteration 2. (c) Iteration 3. K-means 503 (d) Iteration 4. Figure 8.5. Poor starting centroids for K-means. cluster, the centroids will redistribute themselves so that the “true” clusters are found. However, Figure 8.7 shows that if a pair of clusters has only one initial centroid and the other pair has three, then two of the true clusters will be combined and one true cluster will be split. Note that an optimal clustering will be obtained as long as two initial centroids fall anywhere in a pair of clusters, since the centroids will redistribute themselves, one to each cluster. Unfortunately, as the number of clusters becomes larger, it is increasingly likely that at least one pair of clusters will have only one initial centroid. (See Exercise 4 on page 559.) In this case, because the pairs of clusters are farther apart than clusters within a pair, the K-means algorithm will not redistribute the centroids between pairs of clusters, and thus, only a local minimum will be achieved. Because of the problems with using randomly selected initial centroids, which even repeated runs may not overcome, other techniques are often employed for initialization. One eﬀective approach is to take a sample of points and cluster them using a hierarchical clustering technique. K clusters are extracted from the hierarchical clustering, and the centroids of those clusters are used as the initial centroids. This approach often works well, but is practical only if (1) the sample is relatively small, e.g., a few hundred to a few thousand (hierarchical clustering is expensive), and (2) K is relatively small compared to the sample size. The following procedure is another approach to selecting initial centroids. Select the ﬁrst point at random or take the centroid of all points. Then, for each successive initial centroid, select the point that is farthest from any of the initial centroids already selected. In this way, we obtain a set of initial 504 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms (a) Initial points. (b) Iteration 1. (c) Iteration 2. (d) Iteration 3. Figure 8.6. Two pairs of clusters with a pair of initial centroids within each pair of clusters. centroids that is guaranteed to be not only randomly selected but also well separated. Unfortunately, such an approach can select outliers, rather than points in dense regions (clusters). Also, it is expensive to compute the farthest point from the current set of initial centroids. To overcome these problems, this approach is often applied to a sample of the points. Since outliers are rare, they tend not to show up in a random sample. In contrast, points from every dense region are likely to be included unless the sample size is very small. Also, the computation involved in ﬁnding the initial centroids is greatly reduced because the sample size is typically much smaller than the number of points. Later on, we will discuss two other approaches that are useful for producing better-quality (lower SSE) clusterings: using a variant of K-means that 8.2 K-means (a) Iteration 1. (b) Iteration 2. (c) Iteration 3. 505 (d) Iteration 4. Figure 8.7. Two pairs of clusters with more or fewer than two initial centroids within a pair of clusters. is less susceptible to initialization problems (bisecting K-means) and using postprocessing to “ﬁxup” the set of clusters produced. Time and Space Complexity The space requirements for K-means are modest because only the data points and centroids are stored. Speciﬁcally, the storage required is O((m + K )n), where m is the number of points and n is the number of attributes. The time requirements for K-means are also modest—basically linear in the number of data points. In particular, the time required is O(I ∗ K ∗ m ∗ n), where I is the number of iterations required for convergence. As mentioned, I is often small and can usually be safely bounded, as most changes typically occur in the 506 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms ﬁrst few iterations. Therefore, K-means is linear in m, the number of points, and is eﬃcient as well as simple provided that K , the number of clusters, is signiﬁcantly less than m. 8.2.2 K-means: Additional Issues Handling Empty Clusters One of the problems with the basic K-means algorithm given earlier is that empty clusters can be obtained if no points are allocated to a cluster during the assignment step. If this happens, then a strategy is needed to choose a replacement centroid, since otherwise, the squared error will be larger than necessary. One approach is to choose the point that is farthest away from any current centroid. If nothing else, this eliminates the point that currently contributes most to the total squared error. Another approach is to choose the replacement centroid from the cluster that has the highest SSE. This will typically split the cluster and reduce the overall SSE of the clustering. If there are several empty clusters, then this process can be repeated several times. Outliers When the squared error criterion is used, outliers can unduly inﬂuence the clusters that are found. In particular, when outliers are present, the resulting cluster centroids (prototypes) may not be as representative as they otherwise would be and thus, the SSE will be higher as well. Because of this, it is often useful to discover outliers and eliminate them beforehand. It is important, however, to appreciate that there are certain clustering applications for which outliers should not be eliminated. When clustering is used for data compression, every point must be clustered, and in some cases, such as ﬁnancial analysis, apparent outliers, e.g., unusually proﬁtable customers, can be the most interesting points. An obvious issue is how to identify outliers. A number of techniques for identifying outliers will be discussed in Chapter 10. If we use approaches that remove outliers before clustering, we avoid clustering points that will not cluster well. Alternatively, outliers can also be identiﬁed in a postprocessing step. For instance, we can keep track of the SSE contributed by each point, and eliminate those points with unusually high contributions, especially over multiple runs. Also, we may want to eliminate small clusters since they frequently represent groups of outliers. 8.2 K-means 507 Reducing the SSE with Postprocessing An obvious way to reduce the SSE is to ﬁnd more clusters, i.e., to use a larger K . However, in many cases, we would like to improve the SSE, but don’t want to increase the number of clusters. This is often possible because Kmeans typically converges to a local minimum. Various techniques are used to “ﬁx up” the resulting clusters in order to produce a clustering that has lower SSE. The strategy is to focus on individual clusters since the total SSE is simply the sum of the SSE contributed by each cluster. (We will use the terminology total SSE and cluster SSE, respectively, to avoid any potential confusion.) We can change the total SSE by performing various operations on the clusters, such as splitting or merging clusters. One commonly used approach is to use alternate cluster splitting and merging phases. During a splitting phase, clusters are divided, while during a merging phase, clusters are combined. In this way, it is often possible to escape local SSE minima and still produce a clustering solution with the desired number of clusters. The following are some techniques used in the splitting and merging phases. Two strategies that decrease the total SSE by increasing the number of clusters are the following: Split a cluster: The cluster with the largest SSE is usually chosen, but we could also split the cluster with the largest standard deviation for one particular attribute. Introduce a new cluster centroid: Often the point that is farthest from any cluster center is chosen. We can easily determine this if we keep track of the SSE contributed by each point. Another approach is to choose randomly from all points or from the points with the highest SSE. Two strategies that decrease the number of clusters, while trying to minimize the increase in total SSE, are the following: Disperse a cluster: This is accomplished by removing the centroid that corresponds to the cluster and reassigning the points to other clusters. Ideally, the cluster that is dispersed should be the one that increases the total SSE the least. Merge two clusters: The clusters with the closest centroids are typically chosen, although another, perhaps better, approach is to merge the two clusters that result in the smallest increase in total SSE. These two merging strategies are the same ones that are used in the hierarchical 508 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms clustering techniques known as the centroid method and Ward’s method, respectively. Both methods are discussed in Section 8.3. Updating Centroids Incrementally Instead of updating cluster centroids after all points have been assigned to a cluster, the centroids can be updated incrementally, after each assignment of a point to a cluster. Notice that this requires either zero or two updates to cluster centroids at each step, since a point either moves to a new cluster (two updates) or stays in its current cluster (zero updates). Using an incremental update strategy guarantees that empty clusters are not produced since all clusters start with a single point, and if a cluster ever has only one point, then that point will always be reassigned to the same cluster. In addition, if incremental updating is used, the relative weight of the point being added may be adjusted; e.g., the weight of points is often decreased as the clustering proceeds. While this can result in better accuracy and faster convergence, it can be diﬃcult to make a good choice for the relative weight, especially in a wide variety of situations. These update issues are similar to those involved in updating weights for artiﬁcial neural networks. Yet another beneﬁt of incremental updates has to do with using objectives other than “minimize SSE.” Suppose that we are given an arbitrary objective function to measure the goodness of a set of clusters. When we process an individual point, we can compute the value of the objective function for each possible cluster assignment, and then choose the one that optimizes the objective. Speciﬁc examples of alternative objective functions are given in Section 8.5.2. On the negative side, updating centroids incrementally introduces an order dependency. In other words, the clusters produced may depend on the order in which the points are processed. Although this can be addressed by randomizing the order in which the points are processed, the basic K-means approach of updating the centroids after all points have been assigned to clusters has no order dependency. Also, incremental updates are slightly more expensive. However, K-means converges rather quickly, and therefore, the number of points switching clusters quickly becomes relatively small. 8.2.3 Bisecting K-means The bisecting K-means algorithm is a straightforward extension of the basic K-means algorithm that is based on a simple idea: to obtain K clusters, split the set of all points into two clusters, select one of these clusters to split, and 8.2 K-means 509 so on, until K clusters have been produced. The details of bisecting K-means are given by Algorithm 8.2. Algorithm 8.2 Bisecting K-means algorithm. 1: Initialize the list of clusters to contain the cluster consisting of all points. 2: repeat 3: Remove a cluster from the list of clusters. 4: {Perform several “trial” bisections of the chosen cluster.} 5: for i = 1 to number of trials do 6: Bisect the selected cluster using basic K-means. 7: end for 8: Select the two clusters from the bisection with the lowest total SSE. 9: Add these two clusters to the list of clusters. 10: until Until the list of clusters contains K clusters. There are a number of diﬀerent ways to choose which cluster to split. We can choose the largest cluster at each step, choose the one with the largest SSE, or use a criterion based on both size and SSE. Diﬀerent choices result in diﬀerent clusters. We often reﬁne the resulting clusters by using their centroids as the initial centroids for the basic K-means algorithm. This is necessary because, although the K-means algorithm is guaranteed to ﬁnd a clustering that represents a local minimum with respect to the SSE, in bisecting K-means we are using the Kmeans algorithm “locally,” i.e., to bisect individual clusters. Therefore, the ﬁnal set of clusters does not represent a clustering that is a local minimum with respect to the total SSE. Example 8.3 (Bisecting K-means and Initialization). To illustrate that bisecting K-means is less susceptible to initialization problems, we show, in Figure 8.8, how bisecting K-means ﬁnds four clusters in the data set originally shown in Figure 8.6(a). In iteration 1, two pairs of clusters are found; in iteration 2, the rightmost pair of clusters is split; and in iteration 3, the leftmost pair of clusters is split. Bisecting K-means has less trouble with initialization because it performs several trial bisections and takes the one with the lowest SSE, and because there are only two centroids at each step. Finally, by recording the sequence of clusterings produced as K-means bisects clusters, we can also use bisecting K-means to produce a hierarchical clustering. 510 Chapter 8 (a) Iteration 1. Cluster Analysis: Basic Concepts and Algorithms (b) Iteration 2. (c) Iteration 3. Figure 8.8. Bisecting K-means on the four clusters example. 8.2.4 K-means and Diﬀerent Types of Clusters K-means and its variations have a number of limitations with respect to ﬁnding diﬀerent types of clusters. In particular, K-means has diﬃculty detecting the “natural” clusters, when clusters have non-spherical shapes or widely diﬀerent sizes or densities. This is illustrated by Figures 8.9, 8.10, and 8.11. In Figure 8.9, K-means cannot ﬁnd the three natural clusters because one of the clusters is much larger than the other two, and hence, the larger cluster is broken, while one of the smaller clusters is combined with a portion of the larger cluster. In Figure 8.10, K-means fails to ﬁnd the three natural clusters because the two smaller clusters are much denser than the larger cluster. Finally, in Figure 8.11, K-means ﬁnds two clusters that mix portions of the two natural clusters because the shape of the natural clusters is not globular. The diﬃculty in these three situations is that the K-means objective function is a mismatch for the kinds of clusters we are trying to ﬁnd since it is minimized by globular clusters of equal size and density or by clusters that are well separated. However, these limitations can be overcome, in some sense, if the user is willing to accept a clustering that breaks the natural clusters into a number of subclusters. Figure 8.12 shows what happens to the three previous data sets if we ﬁnd six clusters instead of two or three. Each smaller cluster is pure in the sense that it contains only points from one of the natural clusters. 8.2.5 Strengths and Weaknesses K-means is simple and can be used for a wide variety of data types. It is also quite eﬃcient, even though multiple runs are often performed. Some variants, including bisecting K-means, are even more eﬃcient, and are less susceptible to initialization problems. K-means is not suitable for all types of data, 8.2 (a) Original points. K-means (b) Three K-means clusters. Figure 8.9. K-means with clusters of different size. (a) Original points. (b) Three K-means clusters. Figure 8.10. K-means with clusters of different density. (a) Original points. (b) Two K-means clusters. Figure 8.11. K-means with non-globular clusters. 511 512 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms (a) Unequal sizes. (b) Unequal densities. (c) Non-spherical shapes. Figure 8.12. Using K-means to ﬁnd clusters that are subclusters of the natural clusters. 8.2 K-means 513 however. It cannot handle non-globular clusters or clusters of diﬀerent sizes and densities, although it can typically ﬁnd pure subclusters if a large enough number of clusters is speciﬁed. K-means also has trouble clustering data that contains outliers. Outlier detection and removal can help signiﬁcantly in such situations. Finally, K-means is restricted to data for which there is a notion of a center (centroid). A related technique, K-medoid clustering, does not have this restriction, but is more expensive. 8.2.6 K-means as an Optimization Problem Here, we delve into the mathematics behind K-means. This section, which can be skipped without loss of continuity, requires knowledge of calculus through partial derivatives. Familiarity with optimization techniques, especially those based on gradient descent, may also be helpful. As mentioned earlier, given an objective function such as “minimize SSE,” clustering can be treated as an optimization problem. One way to solve this problem—to ﬁnd a global optimum—is to enumerate all possible ways of dividing the points into clusters and then choose the set of clusters that best satisﬁes the objective function, e.g., that minimizes the total SSE. Of course, this exhaustive strategy is computationally infeasible and as a result, a more practical approach is needed, even if such an approach ﬁnds solutions that are not guaranteed to be optimal. One technique, which is known as gradient descent, is based on picking an initial solution and then repeating the following two steps: compute the change to the solution that best optimizes the objective function and then update the solution. We assume that the data is one-dimensional, i.e., dist(x, y ) = (x − y )2 . This does not change anything essential, but greatly simpliﬁes the notation. Derivation of K-means as an Algorithm to Minimize the SSE In this section, we show how the centroid for the K-means algorithm can be mathematically derived when the proximity function is Euclidean distance and the objective is to minimize the SSE. Speciﬁcally, we investigate how we can best update a cluster centroid so that the cluster SSE is minimized. In mathematical terms, we seek to minimize Equation 8.1, which we repeat here, specialized for one-dimensional data. K (ci − x)2 SSE = i=1 x∈Ci (8.4) 514 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms Here, Ci is the ith cluster, x is a point in Ci , and ci is the mean of the ith cluster. See Table 8.1 for a complete list of notation. We can solve for the k th centroid ck , which minimizes Equation 8.4, by diﬀerentiating the SSE, setting it equal to 0, and solving, as indicated below. ∂ SSE = ∂ck ∂ ∂ck K (ci − x)2 i=1 x∈Ci K = i=1 x∈Ci ∂ (ci − x)2 ∂ck 2 ∗ (ck − xk ) = 0 = x∈Ck 2 ∗ (ck − xk ) = 0 ⇒ mk ck = x∈Ck xk ⇒ ck = x∈Ck 1 mk xk x∈Ck Thus, as previously indicated, the best centroid for minimizing the SSE of a cluster is the mean of the points in the cluster. Derivation of K-means for SAE To demonstrate that the K-means algorithm can be applied to a variety of diﬀerent objective functions, we consider how to partition the data into K clusters such that the sum of the Manhattan (L1 ) distances of points from the center of their clusters is minimized. We are seeking to minimize the sum of the L1 absolute errors (SAE) as given by the following equation, where distL1 is the L1 distance. Again, for notational simplicity, we use one-dimensional data, i.e., distL1 = |ci − x|. K distL1 (ci , x) SAE = (8.5) i=1 x∈Ci We can solve for the k th centroid ck , which minimizes Equation 8.5, by diﬀerentiating the SAE, setting it equal to 0, and solving. 8.3 Agglomerative Hierarchical Clustering 515 ∂ SAE = ∂ck ∂ ∂ck K |ci − x| i=1 x∈Ci K = i=1 x∈Ci = x∈Ck x∈Ck ∂ |ck − x| = 0 ⇒ ∂ck ∂ |ci − x| ∂ck ∂ |ck − x| = 0 ∂ck sign(x − ck ) = 0 x∈Ck If we solve for ck , we ﬁnd that ck = median{x ∈ Ck }, the median of the points in the cluster. The median of a group of points is straightforward to compute and less susceptible to distortion by outliers. 8.3 Agglomerative Hierarchical Clustering Hierarchical clustering techniques are a second important category of clustering methods. As with K-means, these approaches are relatively old compared to many clustering algorithms, but they still enjoy widespread use. There are two basic approaches for generating a hierarchical clustering: Agglomerative: Start with the points as individual clusters and, at each step, merge the closest pair of clusters. This requires deﬁning a notion of cluster proximity. Divisive: Start with one, all-inclusive cluster and, at each step, split a cluster until only singleton clusters of individual points remain. In this case, we need to decide which cluster to split at each step and how to do the splitting. Agglomerative hierarchical clustering techniques are by far the most common, and, in this section, we will focus exclusively on these methods. A divisive hierarchical clustering technique is described in Section 9.4.2. A hierarchical clustering is often displayed graphically using a tree-like diagram called a dendrogram, which displays both the cluster-subcluster 516 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms p1 p3 p4 p2 p1 p2 p3 p4 (a) Dendrogram. (b) Nested cluster diagram. Figure 8.13. A hierarchical clustering of four points shown as a dendrogram and as nested clusters. relationships and the order in which the clusters were merged (agglomerative view) or split (divisive view). For sets of two-dimensional points, such as those that we will use as examples, a hierarchical clustering can also be graphically represented using a nested cluster diagram. Figure 8.13 shows an example of these two types of ﬁgures for a set of four two-dimensional points. These points were clustered using the single-link technique that is described in Section 8.3.2. 8.3.1 Basic Agglomerative Hierarchical Clustering Algorithm Many agglomerative hierarchical clustering techniques are variations on a single approach: starting with individual points as clusters, successively merge the two closest clusters until only one cluster remains. This approach is expressed more formally in Algorithm 8.3. Algorithm 8.3 Basic agglomerative hierarchical clustering algorithm. 1: Compute the proximity matrix, if necessary. 2: repeat 3: Merge the closest two clusters. 4: Update the proximity matrix to reﬂect the proximity between the new cluster and the original clusters. 5: until Only one cluster remains. 8.3 Agglomerative Hierarchical Clustering 517 Deﬁning Proximity between Clusters The key operation of Algorithm 8.3 is the computation of the proximity between two clusters, and it is the deﬁnition of cluster proximity that diﬀerentiates the various agglomerative hierarchical techniques that we will discuss. Cluster proximity is typically deﬁned with a particular type of cluster in mind—see Section 8.1.2. For example, many agglomerative hierarchical clustering techniques, such as MIN, MAX, and Group Average, come from a graph-based view of clusters. MIN deﬁnes cluster proximity as the proximity between the closest two points that are in diﬀerent clusters, or using graph terms, the shortest edge between two nodes in diﬀerent subsets of nodes. This yields contiguity-based clusters as shown in Figure 8.2(c). Alternatively, MAX takes the proximity between the farthest two points in diﬀerent clusters to be the cluster proximity, or using graph terms, the longest edge between two nodes in diﬀerent subsets of nodes. (If our proximities are distances, then the names, MIN and MAX, are short and suggestive. For similarities, however, where higher values indicate closer points, the names seem reversed. For that reason, we usually prefer to use the alternative names, single link and complete link, respectively.) Another graph-based approach, the group average technique, deﬁnes cluster proximity to be the average pairwise proximities (average length of edges) of all pairs of points from diﬀerent clusters. Figure 8.14 illustrates these three approaches. (a) MIN (single link.) (b) MAX (complete link.) (c) Group average. Figure 8.14. Graph-based deﬁnitions of cluster proximity If, instead, we take a prototype-based view, in which each cluster is represented by a centroid, diﬀerent deﬁnitions of cluster proximity are more natural. When using centroids, the cluster proximity is commonly deﬁned as the proximity between cluster centroids. An alternative technique, Ward’s method, also assumes that a cluster is represented by its centroid, but it measures the proximity between two clusters in terms of the increase in the SSE that re- 518 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms sults from merging the two clusters. Like K-means, Ward’s method attempts to minimize the sum of the squared distances of points from their cluster centroids. Time and Space Complexity The basic agglomerative hierarchical clustering algorithm just presented uses a proximity matrix. This requires the storage of 1 m2 proximities (assuming 2 the proximity matrix is symmetric) where m is the number of data points. The space needed to keep track of the clusters is proportional to the number of clusters, which is m − 1, excluding singleton clusters. Hence, the total space complexity is O(m2 ). The analysis of the basic agglomerative hierarchical clustering algorithm is also straightforward with respect to computational complexity. O(m2 ) time is required to compute the proximity matrix. After that step, there are m − 1 iterations involving steps 3 and 4 because there are m clusters at the start and two clusters are merged during each iteration. If performed as a linear search of the proximity matrix, then for the ith iteration, step 3 requires O((m − i + 1)2 ) time, which is proportional to the current number of clusters squared. Step 4 only requires O(m − i + 1) time to update the proximity matrix after the merger of two clusters. (A cluster merger aﬀects only O(m − i + 1) proximities for the techniques that we consider.) Without modiﬁcation, this would yield a time complexity of O(m3 ). If the distances from each cluster to all other clusters are stored as a sorted list (or heap), it is possible to reduce the cost of ﬁnding the two closest clusters to O(m − i + 1). However, because of the additional complexity of keeping data in a sorted list or heap, the overall time required for a hierarchical clustering based on Algorithm 8.3 is O(m2 log m). The space and time complexity of hierarchical clustering severely limits the size of data sets that can be processed. We discuss scalability approaches for clustering algorithms, including hierarchical clustering techniques, in Section 9.5. 8.3.2 Speciﬁc Techniques Sample Data To illustrate the behavior of the various hierarchical clustering algorithms, we shall use sample data that consists of 6 two-dimensional points, which are shown in Figure 8.15. The x and y coordinates of the points and the Euclidean distances between them are shown in Tables 8.3 and 8.4, respectively. 8.3 Agglomerative Hierarchical Clustering 519 0.6 1 0.5 0.4 5 Point p1 p2 p3 p4 p5 p6 2 3 0.3 0.2 6 4 0.1 0 0 0.1 0.2 0.3 0.4 0.5 p1 0.00 0.24 0.22 0.37 0.34 0.23 y Coordinate 0.53 0.38 0.32 0.19 0.41 0.30 0.6 Figure 8.15. Set of 6 two-dimensional points. p1 p2 p3 p4 p5 p6 x Coordinate 0.40 0.22 0.35 0.26 0.08 0.45 p2 0.24 0.00 0.15 0.20 0.14 0.25 p3 0.22 0.15 0.00 0.15 0.28 0.11 Table 8.3. xy coordinates of 6 points. p4 0.37 0.20 0.15 0.00 0.29 0.22 p5 0.34 0.14 0.28 0.29 0.00 0.39 p6 0.23 0.25 0.11 0.22 0.39 0.00 Table 8.4. Euclidean distance matrix for 6 points. Single Link or MIN For the single link or MIN version of hierarchical clustering, the proximity of two clusters is deﬁned as the minimum of the distance (maximum of the similarity) between any two points in the two diﬀerent clusters. Using graph terminology, if you start with all points as singleton clusters and add links between points one at a time, shortest links ﬁrst, then these single links combine the points into clusters. The single link technique is good at handling non-elliptical shapes, but is sensitive to noise and outliers. Example 8.4 (Single Link). Figure 8.16 shows the result of applying the single link technique to our example data set of six points. Figure 8.16(a) shows the nested clusters as a sequence of nested ellipses, where the numbers associated with the ellipses indicate the order of the clustering. Figure 8.16(b) shows the same information, but as a dendrogram. The height at which two clusters are merged in the dendrogram reﬂects the distance of the two clusters. For instance, from Table 8.4, we see that the distance between points 3 and 6 520 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 1 5 2 5 3 2 1 3 0.2 0.15 6 0.1 0.05 4 4 0 (a) Single link clustering. 3 6 2 5 4 1 (b) Single link dendrogram. Figure 8.16. Single link clustering of the six points shown in Figure 8.15. is 0.11, and that is the height at which they are joined into one cluster in the dendrogram. As another example, the distance between clusters {3, 6} and {2, 5} is given by dist({3, 6}, {2, 5}) = min(dist(3, 2), dist(6, 2), dist(3, 5), dist(6, 5)) = min(0.15, 0.25, 0.28, 0.39) = 0.15. Complete Link or MAX or CLIQUE For the complete link or MAX version of hierarchical clustering, the proximity of two clusters is deﬁned as the maximum of the distance (minimum of the similarity) between any two points in the two diﬀerent clusters. Using graph terminology, if you start with all points as singleton clusters and add links between points one at a time, shortest links ﬁrst, then a group of points is not a cluster until all the points in it are completely linked, i.e., form a clique. Complete link is less susceptible to noise and outliers, but it can break large clusters and it favors globular shapes. Example 8.5 (Complete Link). Figure 8.17 shows the results of applying MAX to the sample data set of six points. As with single link, points 3 and 6 8.3 4 1 2 5 Agglomerative Hierarchical Clustering 521 5 0.4 0.3 2 3 3 6 1 4 0.2 0.1 0 (a) Complete link clustering. 3 6 4 1 2 5 (b) Complete link dendrogram. Figure 8.17. Complete link clustering of the six points shown in Figure 8.15. are merged ﬁrst. However, {3, 6} is merged with {4}, instead of {2, 5} or {1} because dist({3, 6}, {4}) = max(dist(3, 4), dist(6, 4)) = max(0.15, 0.22) = 0.22. dist({3, 6}, {2, 5}) = max(dist(3, 2), dist(6, 2), dist(3, 5), dist(6, 5)) = max(0.15, 0.25, 0.28, 0.39) = 0.39. dist({3, 6}, {1}) = max(dist(3, 1), dist(6, 1)) = max(0.22, 0.23) = 0.23. Group Average For the group average version of hierarchical clustering, the proximity of two clusters is deﬁned as the average pairwise proximity among all pairs of points in the diﬀerent clusters. This is an intermediate approach between the single and complete link approaches. Thus, for group average, the cluster proxim- 522 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 1 5 2 5 0.25 0.2 2 3 3 6 1 4 0.15 0.1 0.05 4 0 (a) Group average clustering. 3 6 4 2 5 1 (b) Group average dendrogram. Figure 8.18. Group average clustering of the six points shown in Figure 8.15. ity proximity (Ci , Cj ) of clusters Ci and Cj , which are of size mi and mj , respectively, is expressed by the following equation: proximity (Ci , Cj ) = x∈Ci y∈Cj proximity (x, y) mi ∗ mj . (8.6) Example 8.6 (Group Average). Figure 8.18 shows the results of applying the group average approach to the sample data set of six points. To illustrate how group average works, we calculate the distance between some clusters. dist({3, 6, 4}, {1}) = (0.22 + 0.37 + 0.23)/(3 ∗ 1) = 0.28 dist({2, 5}, {1}) = (0.2357 + 0.3421)/(2 ∗ 1) = 0.2889 dist({3, 6, 4}, {2, 5}) = (0.15 + 0.28 + 0.25 + 0.39 + 0.20 + 0.29)/(6 ∗ 2) = 0.26 Because dist({3, 6, 4}, {2, 5}) is smaller than dist({3, 6, 4}, {1}) and dist({2, 5}, {1}), clusters {3, 6, 4} and {2, 5} are merged at the fourth stage. 8.3 5 4 1 2 5 Agglomerative Hierarchical Clustering 523 0.25 0.2 2 3 6 1 4 3 0.15 0.1 0.05 0 (a) Ward’s clustering. 3 6 4 1 2 5 (b) Ward’s dendrogram. Figure 8.19. Ward’s clustering of the six points shown in Figure 8.15. Ward’s Method and Centroid Methods For Ward’s method, the proximity between two clusters is deﬁned as the increase in the squared error that results when two clusters are merged. Thus, this method uses the same objective function as K-means clustering. While it may seem that this feature makes Ward’s method somewhat distinct from other hierarchical techniques, it can be shown mathematically that Ward’s method is very similar to the group average method when the proximity between two points is taken to be the square of the distance between them. Example 8.7 (Ward’s Method). Figure 8.19 shows the results of applying Ward’s method to the sample data set of six points. The clustering that is produced is diﬀerent from those produced by single link, complete link, and group average. Centroid methods calculate the proximity between two clusters by calculating the distance between the centroids of clusters. These techniques may seem similar to K-means, but as we have remarked, Ward’s method is the correct hierarchical analog. Centroid methods also have a characteristic—often considered bad—that is not possessed by the other hierarchical clustering techniques that we have discussed: the possibility of inversions. Speciﬁcally, two clusters that are merged may be more similar (less distant) than the pair of clusters that were merged in a previous step. For the other methods, the distance between 524 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms Table 8.5. Table of Lance-Williams coefﬁcients for common hierarchical clustering approaches. Clustering Method Single Link Complete Link Group Average Centroid Ward’s αA 1/2 1/2 mA mA + mB mA mA + mB mA + mQ mA + mB + mQ αB 1/2 1/2 mB mA + mB mB mA + mB mB + mQ mA + mB + m Q β 0 0 0 −mA mB ( mA + mB ) 2 −mQ mA + mB + mQ γ −1/2 1/2 0 0 0 merged clusters monotonically increases (or is, at worst, non-increasing) as we proceed from singleton clusters to one all-inclusive cluster. 8.3.3 The Lance-Williams Formula for Cluster Proximity Any of the cluster proximities that we have discussed in this section can be viewed as a choice of diﬀerent parameters (in the Lance-Williams formula shown below in Equation 8.7) for the proximity between clusters Q and R, where R is formed by merging clusters A and B . In this equation, p(., .) is a proximity function, while mA , mB , and mQ are the number of points in clusters A, B , and Q, respectively. In other words, after we merge clusters A and B to form cluster R, the proximity of the new cluster, R, to an existing cluster, Q, is a linear function of the proximities of Q with respect to the original clusters A and B . Table 8.5 shows the values of these coeﬃcients for the techniques that we have discussed. p(R, Q) = αA p(A, Q) + αB p(B, Q) + β p(A, B ) + γ |p(A, Q) − p(B, Q)| (8.7) Any hierarchical clustering technique that can be expressed using the Lance-Williams formula does not need to keep the original data points. Instead, the proximity matrix is updated as clustering occurs. While a general formula is appealing, especially for implementation, it is easier to understand the diﬀerent hierarchical methods by looking directly at the deﬁnition of cluster proximity that each method uses. 8.3.4 Key Issues in Hierarchical Clustering Lack of a Global Objective Function We previously mentioned that agglomerative hierarchical clustering cannot be viewed as globally optimizing an objective function. Instead, agglomerative hierarchical clustering techniques use various criteria to decide locally, at each 8.3 Agglomerative Hierarchical Clustering 525 step, which clusters should be merged (or split for divisive approaches). This approach yields clustering algorithms that avoid the diﬃculty of attempting to solve a hard combinatorial optimization problem. (It can be shown that the general clustering problem for an objective function such as “minimize SSE” is computationally infeasible.) Furthermore, such approaches do not have problems with local minima or diﬃculties in choosing initial points. Of course, the time complexity of O(m2 log m) and the space complexity of O(m2 ) are prohibitive in many cases. Ability to Handle Diﬀerent Cluster Sizes One aspect of agglomerative hierarchical clustering that we have not yet discussed is how to treat the relative sizes of the pairs of clusters that are merged. (This discussion applies only to cluster proximity schemes that involve sums, such as centroid, Ward’s, and group average.) There are two approaches: weighted, which treats all clusters equally, and unweighted, which takes the number of points in each cluster into account. Note that the terminology of weighted or unweighted refers to the data points, not the clusters. In other words, treating clusters of unequal size equally gives diﬀerent weights to the points in diﬀerent clusters, while taking the cluster size into account gives points in diﬀerent clusters the same weight. We will illustrate this using the group average technique discussed in Section 8.3.2, which is the unweighted version of the group average technique. In the clustering literature, the full name of this approach is the Unweighted Pair Group Method using Arithmetic averages (UPGMA). In Table 8.5, which gives the formula for updating cluster similarity, the coeﬃcients for UPGMA mA involve the size of each of the clusters that were merged: αA = mA +mB , αB = mB mA +mB , β = 0, γ = 0. For the weighted version of group average—known as WPGMA—the coeﬃcients are constants: αA = 1/2, αB = 1/2, β = 0, γ = 0. In general, unweighted approaches are preferred unless there is reason to believe that individual points should have diﬀerent weights; e.g., perhaps classes of objects have been unevenly sampled. Merging Decisions Are Final Agglomerative hierarchical clustering algorithms tend to make good local decisions about combining two clusters since they can use information about the pairwise similarity of all points. However, once a decision is made to merge two clusters, it cannot be undone at a later time. This approach prevents a local optimization criterion from becoming a global optimization criterion. 526 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms For example, although the “minimize squared error” criterion from K-means is used in deciding which clusters to merge in Ward’s method, the clusters at each level do not represent local minima with respect to the total SSE. Indeed, the clusters are not even stable, in the sense that a point in one cluster may be closer to the centroid of some other cluster than it is to the centroid of its current cluster. Nonetheless, Ward’s method is often used as a robust method of initializing a K-means clustering, indicating that a local “minimize squared error” objective function does have a connection to a global “minimize squared error” objective function. There are some techniques that attempt to overcome the limitation that merges are ﬁnal. One approach attempts to ﬁx up the hierarchical clustering by moving branches of the tree around so as to improve a global objective function. Another approach uses a partitional clustering technique such as Kmeans to create many small clusters, and then performs hierarchical clustering using these small clusters as the starting point. 8.3.5 Strengths and Weaknesses The strengths and weakness of speciﬁc agglomerative hierarchical clustering algorithms were discussed above. More generally, such algorithms are typically used because the underlying application, e.g., creation of a taxonomy, requires a hierarchy. Also, there have been some studies that suggest that these algorithms can produce better-quality clusters. However, agglomerative hierarchical clustering algorithms are expensive in terms of their computational and storage requirements. The fact that all merges are ﬁnal can also cause trouble for noisy, high-dimensional data, such as document data. In turn, these two problems can be addressed to some degree by ﬁrst partially clustering the data using another technique, such as K-means. 8.4 DBSCAN Density-based clustering locates regions of high density that are separated from one another by regions of low density. DBSCAN is a simple and eﬀective density-based clustering algorithm that illustrates a number of important concepts that are important for any density-based clustering approach. In this section, we focus solely on DBSCAN after ﬁrst considering the key notion of density. Other algorithms for ﬁnding density-based clusters are described in the next chapter. 8.4 8.4.1 DBSCAN 527 Traditional Density: Center-Based Approach Although there are not as many approaches for deﬁning density as there are for deﬁning similarity, there are several distinct methods. In this section we discuss the center-based approach on which DBSCAN is based. Other deﬁnitions of density will be presented in Chapter 9. In the center-based approach, density is estimated for a particular point in the data set by counting the number of points within a speciﬁed radius, Eps, of that point. This includes the point itself. This technique is graphically illustrated by Figure 8.20. The number of points within a radius of Eps of point A is 7, including A itself. This method is simple to implement, but the density of any point will depend on the speciﬁed radius. For instance, if the radius is large enough, then all points will have a density of m, the number of points in the data set. Likewise, if the radius is too small, then all points will have a density of 1. An approach for deciding on the appropriate radius for low-dimensional data is given in the next section in the context of our discussion of DBSCAN. Classiﬁcation of Points According to Center-Based Density The center-based approach to density allows us to classify a point as being (1) in the interior of a dense region (a core point), (2) on the edge of a dense region (a border point), or (3) in a sparsely occupied region (a noise or background point). Figure 8.21 graphically illustrates the concepts of core, border, and noise points using a collection of two-dimensional points. The following text provides a more precise description. Core points: These points are in the interior of a density-based cluster. A point is a core point if the number of points within a given neighborhood around the point as determined by the distance function and a userspeciﬁed distance parameter, Eps, exceeds a certain threshold, M inP ts, which is also a user-speciﬁed parameter. In Figure 8.21, point A is a core point, for the indicated radius (Eps) if M inP ts ≤ 7. Border points: A border point is not a core point, but falls within the neighborhood of a core point. In Figure 8.21, point B is a border point. A border point can fall within the neighborhoods of several core points. Noise points: A noise point is any point that is neither a core point nor a border point. In Figure 8.21, point C is a noise point. 528 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms core point border point noise point A B Eps A C Eps Eps Eps Figure 8.20. density. 8.4.2 Center-based Figure 8.21. Core, border, and noise points. The DBSCAN Algorithm Given the previous deﬁnitions of core points, border points, and noise points, the DBSCAN algorithm can be informally described as follows. Any two core points that are close enough—within a distance Eps of one another—are put in the same cluster. Likewise, any border point that is close enough to a core point is put in the same cluster as the core point. (Ties may need to be resolved if a border point is close to core points from diﬀerent clusters.) Noise points are discarded. The formal details are given in Algorithm 8.4. This algorithm uses the same concepts and ﬁnds the same clusters as the original DBSCAN, but is optimized for simplicity, not eﬃciency. Algorithm 8.4 DBSCAN algorithm. 1: 2: 3: 4: 5: Label all points as core, border, or noise points. Eliminate noise points. Put an edge between all core points that are within Eps of each other. Make each group of connected core points into a separate cluster. Assign each border point to one of the clusters of its associated core points. Time and Space Complexity The basic time complexity of the DBSCAN algorithm is O(m × time to ﬁnd points in the Eps-neighborhood), where m is the number of points. In the worst case, this complexity is O(m2 ). However, in low-dimensional spaces, there are data structures, such as kd-trees, that allow eﬃcient retrieval of all 8.4 DBSCAN 529 points within a given distance of a speciﬁed point, and the time complexity can be as low as O(m log m). The space requirement of DBSCAN, even for high-dimensional data, is O(m) because it is only necessary to keep a small amount of data for each point, i.e., the cluster label and the identiﬁcation of each point as a core, border, or noise point. Selection of DBSCAN Parameters There is, of course, the issue of how to determine the parameters Eps and M inP ts. The basic approach is to look at the behavior of the distance from a point to its k th nearest neighbor, which we will call the k -dist. For points that belong to some cluster, the value of k -dist will be small if k is not larger than the cluster size. Note that there will be some variation, depending on the density of the cluster and the random distribution of points, but on average, the range of variation will not be huge if the cluster densities are not radically diﬀerent. However, for points that are not in a cluster, such as noise points, the k -dist will be relatively large. Therefore, if we compute the k -dist for all the data points for some k , sort them in increasing order, and then plot the sorted values, we expect to see a sharp change at the value of k -dist that corresponds to a suitable value of Eps. If we select this distance as the Eps parameter and take the value of k as the M inP ts parameter, then points for which k -dist is less than Eps will be labeled as core points, while other points will be labeled as noise or border points. Figure 8.22 shows a sample data set, while the k -dist graph for the data is given in Figure 8.23. The value of Eps that is determined in this way depends on k , but does not change dramatically as k changes. If the value of k is too small, then even a small number of closely spaced points that are noise or outliers will be incorrectly labeled as clusters. If the value of k is too large, then small clusters (of size less than k ) are likely to be labeled as noise. The original DBSCAN algorithm used a value of k = 4, which appears to be a reasonable value for most two-dimensional data sets. Clusters of Varying Density DBSCAN can have trouble with density if the density of clusters varies widely. Consider Figure 8.24, which shows four clusters embedded in noise. The density of the clusters and noise regions is indicated by their darkness. The noise around the pair of denser clusters, A and B , has the same density as clusters C and D. If the Eps threshold is low enough that DBSCAN ﬁnds C and D as clusters, then A and B and the points surrounding them will become a single 530 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 4th Nearest Neighbor Distance 50 40 30 20 10 0 0 500 1000 1500 2000 2500 3000 Points Sorted by Distance to 4th Nearest Neighbor Figure 8.22. Sample data. Cluster A Figure 8.23. K-dist plot for sample data. Cluster B Noise Cluster C Cluster D Noise Figure 8.24. Four clusters embedded in noise. cluster. If the Eps threshold is high enough that DBSCAN ﬁnds A and B as separate clusters, and the points surrounding them are marked as noise, then C and D and the points surrounding them will also be marked as noise. An Example To illustrate the use of DBSCAN, we show the clusters that it ﬁnds in the relatively complicated two-dimensional data set shown in Figure 8.22. This data set consists of 3000 two-dimensional points. The Eps threshold for this data was found by plotting the sorted distances of the fourth nearest neighbor of each point (Figure 8.23) and identifying the value at which there is a sharp increase. We selected Eps = 10, which corresponds to the knee of the curve. The clusters found by DBSCAN using these parameters, i.e., M inP ts = 4 and Eps = 10, are shown in Figure 8.25(a). The core points, border points, and noise points are displayed in Figure 8.25(b). 8.4.3 Strengths and Weaknesses Because DBSCAN uses a density-based deﬁnition of a cluster, it is relatively resistant to noise and can handle clusters of arbitrary shapes and sizes. Thus, 8.4 DBSCAN (a) Clusters found by DBSCAN. x – Noise Point + – Border Point – Core Point (b) Core, border, and noise points. Figure 8.25. DBSCAN clustering of 3000 two-dimensional points. 531 532 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms DBSCAN can ﬁnd many clusters that could not be found using K-means, such as those in Figure 8.22. As indicated previously, however, DBSCAN has trouble when the clusters have widely varying densities. It also has trouble with high-dimensional data because density is more diﬃcult to deﬁne for such data. One possible approach to dealing with such issues is given in Section 9.4.8. Finally, DBSCAN can be expensive when the computation of nearest neighbors requires computing all pairwise proximities, as is usually the case for high-dimensional data. 8.5 Cluster Evaluation In supervised classiﬁcation, the evaluation of the resulting classiﬁcation model is an integral part of the process of developing a classiﬁcation model, and there are well-accepted evaluation measures and procedures, e.g., accuracy and cross-validation, respectively. However, because of its very nature, cluster evaluation is not a well-developed or commonly used part of cluster analysis. Nonetheless, cluster evaluation, or cluster validation as it is more traditionally called, is important, and this section will review some of the most common and easily applied approaches. There might be some confusion as to why cluster evaluation is necessary. Many times, cluster analysis is conducted as a part of an exploratory data analysis. Hence, evaluation seems like an unnecessarily complicated addition to what is supposed to be an informal process. Furthermore, since there are a number of diﬀerent types of clusters—in some sense, each clustering algorithm deﬁnes its own type of cluster—it may seem that each situation might require a diﬀerent evaluation measure. For instance, K-means clusters might be evaluated in terms of the SSE, but for density-based clusters, which need not be globular, SSE would not work well at all. Nonetheless, cluster evaluation should be a part of any cluster analysis. A key motivation is that almost every clustering algorithm will ﬁnd clusters in a data set, even if that data set has no natural cluster structure. For instance, consider Figure 8.26, which shows the result of clustering 100 points that are randomly (uniformly) distributed on the unit square. The original points are shown in Figure 8.26(a), while the clusters found by DBSCAN, Kmeans, and complete link are shown in Figures 8.26(b), 8.26(c), and 8.26(d), respectively. Since DBSCAN found three clusters (after we set Eps by looking at the distances of the fourth nearest neighbors), we set K-means and complete link to ﬁnd three clusters as well. (In Figure 8.26(b) the noise is shown by the small markers.) However, the clusters do not look compelling for any of 8.5 Cluster Evaluation 533 the three methods. In higher dimensions, such problems cannot be so easily detected. 8.5.1 Overview Being able to distinguish whether there is non-random structure in the data is just one important aspect of cluster validation. The following is a list of several important issues for cluster validation. 1. Determining the clustering tendency of a set of data, i.e., distinguishing whether non-random structure actually exists in the data. 2. Determining the correct number of clusters. 3. Evaluating how well the results of a cluster analysis ﬁt the data without reference to external information. 4. Comparing the results of a cluster analysis to externally known results, such as externally provided class labels. 5. Comparing two sets of clusters to determine which is better. Notice that items 1, 2, and 3 do not make use of any external information— they are unsupervised techniques—while item 4 requires external information. Item 5 can be performed in either a supervised or an unsupervised manner. A further distinction can be made with respect to items 3, 4, and 5: Do we want to evaluate the entire clustering or just individual clusters? While it is possible to develop various numerical measures to assess the diﬀerent aspects of cluster validity mentioned above, there are a number of challenges. First, a measure of cluster validity may be quite limited in the scope of its applicability. For example, most work on measures of clustering tendency has been done for two- or three-dimensional spatial data. Second, we need a framework to interpret any measure. If we obtain a value of 10 for a measure that evaluates how well cluster labels match externally provided class labels, does this value represent a good, fair, or poor match? The goodness of a match often can be measured by looking at the statistical distribution of this value, i.e., how likely it is that such a value occurs by chance. Finally, if a measure is too complicated to apply or to understand, then few will use it. The evaluation measures, or indices, that are applied to judge various aspects of cluster validity are traditionally classiﬁed into the following three types. 534 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms (a) Original points. (b) Three clusters found by DBSCAN. (c) Three clusters found by K-means. (d) Three clusters found by complete link. Figure 8.26. Clustering of 100 uniformly distributed points. 8.5 Cluster Evaluation 535 Unsupervised. Measures the goodness of a clustering structure without respect to external information. An example of this is the SSE. Unsupervised measures of cluster validity are often further divided into two classes: measures of cluster cohesion (compactness, tightness), which determine how closely related the objects in a cluster are, and measures of cluster separation (isolation), which determine how distinct or wellseparated a cluster is from other clusters. Unsupervised measures are often called internal indices because they use only information present in the data set. Supervised. Measures the extent to which the clustering structure discovered by a clustering algorithm matches some external structure. An example of a supervised index is entropy, which measures how well cluster labels match externally supplied class labels. Supervised measures are often called external indices because they use information not present in the data set. Relative. Compares diﬀerent clusterings or clusters. A relative cluster evaluation measure is a supervised or unsupervised evaluation measure that is used for the purpose of comparison. Thus, relative measures are not actually a separate type of cluster evaluation measure, but are instead a speciﬁc use of such measures. As an example, two K-means clusterings can be compared using either the SSE or entropy. In the remainder of this section, we provide speciﬁc details concerning cluster validity. We ﬁrst describe topics related to unsupervised cluster evaluation, beginning with (1) measures based on cohesion and separation, and (2) two techniques based on the proximity matrix. Since these approaches are useful only for partitional sets of clusters, we also describe the popular cophenetic correlation coeﬃcient, which can be used for the unsupervised evaluation of a hierarchical clustering. We end our discussion of unsupervised evaluation with brief discussions about ﬁnding the correct number of clusters and evaluating clustering tendency. We then consider supervised approaches to cluster validity, such as entropy, purity, and the Jaccard measure. We conclude this section with a short discussion of how to interpret the values of (unsupervised or supervised) validity measures. 536 Chapter 8 8.5.2 Cluster Analysis: Basic Concepts and Algorithms Unsupervised Cluster Evaluation Using Cohesion and Separation Many internal measures of cluster validity for partitional clustering schemes are based on the notions of cohesion or separation. In this section, we use cluster validity measures for prototype- and graph-based clustering techniques to explore these notions in some detail. In the process, we will also see some interesting relationships between prototype- and graph-based clustering. In general, we can consider expressing overall cluster validity for a set of K clusters as a weighted sum of the validity of individual clusters, K overall validity = wi validity (Ci ). (8.8) i=1 The validity function can be cohesion, separation, or some combination of these quantities. The weights will vary depending on the cluster validity measure. In some cases, the weights are simply 1 or the size of the cluster, while in other cases they reﬂect a more complicated property, such as the square root of the cohesion. See Table 8.6. If the validity function is cohesion, then higher values are better. If it is separation, then lower values are better. Graph-Based View of Cohesion and Separation For graph-based clusters, the cohesion of a cluster can be deﬁned as the sum of the weights of the links in the proximity graph that connect points within the cluster. See Figure 8.27(a). (Recall that the proximity graph has data objects as nodes, a link between each pair of data objects, and a weight assigned to each link that is the proximity between the two data objects connected by the link.) Likewise, the separation between two clusters can be measured by the sum of the weights of the links from points in one cluster to points in the other cluster. This is illustrated in Figure 8.27(b). Mathematically, cohesion and separation for a graph-based cluster can be expressed using Equations 8.9 and 8.10, respectively. The proximity function can be a similarity, a dissimilarity, or a simple function of these quantities. cohesion(Ci ) = proximity (x, y) (8.9) proximity (x, y) (8.10) x∈Ci y∈Ci separation(Ci , Cj ) = x∈Ci y∈Cj 8.5 (a) Cohesion. Cluster Evaluation 537 (b) Separation. Figure 8.27. Graph-based view of cluster cohesion and separation. Prototype-Based View of Cohesion and Separation For prototype-based clusters, the cohesion of a cluster can be deﬁned as the sum of the proximities with respect to the prototype (centroid or medoid) of the cluster. Similarly, the separation between two clusters can be measured by the proximity of the two cluster prototypes. This is illustrated in Figure 8.28, where the centroid of a cluster is indicated by a “+”. Cohesion for a prototype-based cluster is given in Equation 8.11, while two measures for separation are given in Equations 8.12 and 8.13, respectively, where ci is the prototype (centroid) of cluster Ci and c is the overall prototype (centroid). There are two measures for separation because, as we will see shortly, the separation of cluster prototypes from an overall prototype is sometimes directly related to the separation of cluster prototypes from one another. Note that Equation 8.11 is the cluster SSE if we let proximity be the squared Euclidean distance. cohesion(Ci ) = proximity (x, ci ) (8.11) x∈Ci separation(Ci , Cj ) = proximity (ci , cj ) separation(Ci ) = proximity (ci , c) (8.12) (8.13) Overall Measures of Cohesion and Separation The previous deﬁnitions of cluster cohesion and separation gave us some simple and well-deﬁned measures of cluster validity that can be combined into an overall measure of cluster validity by using a weighted sum, as indicated 538 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms + + (a) Cohesion. + (b) Separation. Figure 8.28. Prototype-based view of cluster cohesion and separation. in Equation 8.8. However, we need to decide what weights to use. Not surprisingly, the weights used can vary widely, although typically they are some measure of cluster size. Table 8.6 provides examples of validity measures based on cohesion and separation. I1 is a measure of cohesion in terms of the pairwise proximity of objects in the cluster divided by the cluster size. I2 is a measure of cohesion based on the sum of the proximities of objects in the cluster to the cluster centroid. E1 is a measure of separation deﬁned as the proximity of a cluster centroid to the overall centroid multiplied by the number of objects in the cluster. G1 , which is a measure based on both cohesion and separation, is the sum of the pairwise proximity of all objects in the cluster with all objects outside the cluster—the total weight of the edges of the proximity graph that must be cut to separate the cluster from all other clusters—divided by the sum of the pairwise proximity of objects in the cluster. Table 8.6. Table of graph-based cluster evaluation measures. Name Cluster Measure Cluster Weight I1 1 mi I2 E1 G1 x∈Ci y∈Ci proximity (x, y) x∈Ci proximity (x, ci ) proximity (ci , c) k j =1 j =i x∈Ci y∈Cj proximity (x, y) 1 mi x∈Ci y∈Ci Type graph-based cohesion prototype-based cohesion prototype-based separation graph-based 1 separation and proximity (x, y) cohesion 8.5 Cluster Evaluation 539 Note that any unsupervised measure of cluster validity potentially can be used as an objective function for a clustering algorithm and vice versa. The CLUstering TOolkit (CLUTO) (see the bibliographic notes) uses the cluster evaluation measures described in Table 8.6, as well as some other evaluation measures not mentioned here, to drive the clustering process. It does this by using an algorithm that is similar to the incremental K-means algorithm discussed in Section 8.2.2. Speciﬁcally, each point is assigned to the cluster that produces the best value for the cluster evaluation function. The cluster evaluation measure I2 corresponds to traditional K-means and produces clusters that have good SSE values. The other measures produce clusters that are not as good with respect to SSE, but that are more optimal with respect to the speciﬁed cluster validity measure. Relationship between Prototype-Based Cohesion and Graph-Based Cohesion While the graph-based and prototype-based approaches to measuring the cohesion and separation of a cluster seem distinct, for some proximity measures they are equivalent. For instance, for the SSE and points in Euclidean space, it can be shown (Equation 8.14) that the average pairwise distance between the points in a cluster is equivalent to the SSE of the cluster. See Exercise 27 on page 566. dist(ci , x)2 = Cluster SSE = x∈Ci 1 2mi dist(x, y)2 (8.14) x∈Ci y∈Ci Two Approaches to Prototype-Based Separation When proximity is measured by Euclidean distance, the traditional measure of separation between clusters is the between group sum of squares (SSB), which is the sum of the squared distance of a cluster centroid, ci , to the overall mean, c, of all the data points. By summing the SSB over all clusters, we obtain the total SSB, which is given by Equation 8.15, where ci is the mean of the ith cluster and c is the overall mean. The higher the total SSB of a clustering, the more separated the clusters are from one another. K Total SSB = mi dist(ci , c)2 (8.15) i=1 It is straightforward to show that the total SSB is directly related to the pairwise distances between the centroids. In particular, if the cluster sizes are 540 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms equal, i.e., mi = m/K , then this relationship takes the simple form given by Equation 8.16. (See Exercise 28 on page 566.) It is this type of equivalence that motivates the deﬁnition of prototype separation in terms of both Equations 8.12 and 8.13. Total SSB = K 1 2K K i=1 j =1 m dist(ci , cj )2 K (8.16) Relationship between Cohesion and Separation In some cases, there is also a strong relationship between cohesion and separation. Speciﬁcally, it is possible to show that the sum of the total SSE and the total SSB is a constant; i.e., that it is equal to the total sum of squares (TSS), which is the sum of squares of the distance of each point to the overall mean of the data. The importance of this result is that minimizing SSE (cohesion) is equivalent to maximizing SSB (separation). We provide the proof of this fact below, since the approach illustrates techniques that are also applicable to proving the relationships stated in the last two sections. To simplify the notation, we assume that the data is onedimensional, i.e., dist(x, y ) = (x−y )2 . Also, we use the fact that the cross-term K i=1 x∈Ci (x − ci )(c − ci ) is 0. (See Exercise 29 on page 566.) K TSS = (x − c)2 i=1 x∈Ci K = ((x − ci ) − (c − ci ))2 i=1 x∈Ci K = (x − ci )2 − 2 i=1 x∈Ci K = (x − ci )2 + K (c − ci )2 i=1 x∈Ci K 2 K (x − ci ) + = K (x − ci )(c − ci ) + i=1 x∈Ci i=1 x∈Ci i=1 x∈Ci = K SSE + SSB i=1 |Ci |(c − ci )2 i=1 x∈Ci (c − ci )2 8.5 Cluster Evaluation 541 Evaluating Individual Clusters and Objects So far, we have focused on using cohesion and separation in the overall evaluation of a group of clusters. Many of these measures of cluster validity also can be used to evaluate individual clusters and objects. For example, we can rank individual clusters according to their speciﬁc value of cluster validity, i.e., cluster cohesion or separation. A cluster that has a high value of cohesion may be considered better than a cluster that has a lower value. This information often can be used to improve the quality of a clustering. If, for example, a cluster is not very cohesive, then we may want to split it into several subclusters. On the other hand, if two clusters are relatively cohesive, but not well separated, we may want to merge them into a single cluster. We can also evaluate the objects within a cluster in terms of their contribution to the overall cohesion or separation of the cluster. Objects that contribute more to the cohesion and separation are near the “interior” of the cluster. Those objects for which the opposite is true are probably near the “edge” of the cluster. In the following section, we consider a cluster evaluation measure that uses an approach based on these ideas to evaluate points, clusters, and the entire set of clusters. The Silhouette Coeﬃcient The popular method of silhouette coeﬃcients combines both cohesion and separation. The following steps explain how to compute the silhouette coeﬃcient for an individual point, a process that consists of the following three steps. We use distances, but an analogous approach can be used for similarities. 1. For the ith object, calculate its average distance to all other objects in its cluster. Call this value ai . 2. For the ith object and any cluster not containing the object, calculate the object’s average distance to all the objects in the given cluster. Find the minimum such value with respect to all clusters; call this value bi . 3. For the ith object, the silhouette coeﬃcient is si = (bi − ai )/ max(ai , bi ). The value of the silhouette coeﬃcient can vary between −1 and 1. A negative value is undesirable because this corresponds to a case in which ai , the average distance to points in the cluster, is greater than bi , the minimum average distance to points in another cluster. We want the silhouette coeﬃcient to be positive (ai < bi ), and for ai to be as close to 0 as possible, since the coeﬃcient assumes its maximum value of 1 when ai = 0. 542 Chapter 8 0 0.1 Cluster Analysis: Basic Concepts and Algorithms 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Silhouette Coefficient Figure 8.29. Silhouette coefﬁcients for points in ten clusters. We can compute the average silhouette coeﬃcient of a cluster by simply taking the average of the silhouette coeﬃcients of points belonging to the cluster. An overall measure of the goodness of a clustering can be obtained by computing the average silhouette coeﬃcient of all points. Example 8.8 (Silhouette Coeﬃcient). Figure 8.29 shows a plot of the silhouette coeﬃcients for points in 10 clusters. Darker shades indicate lower silhouette coeﬃcients. 8.5.3 Unsupervised Cluster Evaluation Using the Proximity Matrix In this section, we examine a couple of unsupervised approaches for assessing cluster validity that are based on the proximity matrix. The ﬁrst compares an actual and idealized proximity matrix, while the second uses visualization. Measuring Cluster Validity via Correlation If we are given the similarity matrix for a data set and the cluster labels from a cluster analysis of the data set, then we can evaluate the “goodness” of the clustering by looking at the correlation between the similarity matrix and an ideal version of the similarity matrix based on the cluster labels. (With minor changes, the following applies to proximity matrices, but for simplicity, we discuss only similarity matrices.) More speciﬁcally, an ideal cluster is one whose points have a similarity of 1 to all points in the cluster, and a similarity of 0 to all points in other clusters. Thus, if we sort the rows and columns of the similarity matrix so that all objects belonging to the same class are together, then an ideal similarity matrix has a block diagonal structure. In other words, the similarity is non-zero, i.e., 1, inside the blocks of the similarity 8.5 Cluster Evaluation 543 matrix whose entries represent intra-cluster similarity, and 0 elsewhere. The ideal similarity matrix is constructed by creating a matrix that has one row and one column for each data point—just like an actual similarity matrix— and assigning a 1 to an entry if the associated pair of points belongs to the same cluster. All other entries are 0. High correlation between the ideal and actual similarity matrices indicates that the points that belong to the same cluster are close to each other, while low correlation indicates the opposite. (Since the actual and ideal similarity matrices are symmetric, the correlation is calculated only among the n(n − 1)/2 entries below or above the diagonal of the matrices.) Consequently, this is not a good measure for many density- or contiguity-based clusters, because they are not globular and may be closely intertwined with other clusters. Example 8.9 (Correlation of Actual and Ideal Similarity Matrices). To illustrate this measure, we calculated the correlation between the ideal and actual similarity matrices for the K-means clusters shown in Figure 8.26(c) (random data) and Figure 8.30(a) (data with three well-separated clusters). The correlations were 0.5810 and 0.9235, respectively, which reﬂects the expected result that the clusters found by K-means in the random data are worse than the clusters found by K-means in data with well-separated clusters. Judging a Clustering Visually by Its Similarity Matrix The previous technique suggests a more general, qualitative approach to judging a set of clusters: Order the similarity matrix with respect to cluster labels and then plot it. In theory, if we have well-separated clusters, then the similarity matrix should be roughly block-diagonal. If not, then the patterns displayed in the similarity matrix can reveal the relationships between clusters. Again, all of this can be applied to dissimilarity matrices, but for simplicity, we will only discuss similarity matrices. Example 8.10 (Visualizing a Similarity Matrix). Consider the points in Figure 8.30(a), which form three well-separated clusters. If we use K-means to group these points into three clusters, then we should have no trouble ﬁnding these clusters since they are well-separated. The separation of these clusters is illustrated by the reordered similarity matrix shown in Figure 8.30(b). (For uniformity, we have transformed the distances into similarities using the formula s = 1 − (d − min d)/(max d − min d).) Figure 8.31 shows the reordered similarity matrices for clusters found in the random data set of Figure 8.26 by DBSCAN, K-means, and complete link. 544 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 1 1 10 0.9 20 0.8 30 0.7 40 0.6 50 0.5 60 0.4 70 0.3 80 0.2 0.8 y Points 0.6 0.4 0.2 90 0 0 0.2 0.4 0.6 0.8 x (a) Well-separated clusters. 1 100 0.1 20 40 60 80 100 0 Similarity Points (b) Similarity matrix sorted by K-means cluster labels. Figure 8.30. Similarity matrix for well-separated clusters. The well-separated clusters in Figure 8.30 show a very strong, blockdiagonal pattern in the reordered similarity matrix. However, there are also weak block diagonal patterns—see Figure 8.31—in the reordered similarity matrices of the clusterings found by K-means, DBSCAN, and complete link in the random data. Just as people can ﬁnd patterns in clouds, data mining algorithms can ﬁnd clusters in random data. While it is entertaining to ﬁnd patterns in clouds, it is pointless and perhaps embarrassing to ﬁnd clusters in noise. This approach may seem hopelessly expensive for large data sets, since the computation of the proximity matrix takes O(m2 ) time, where m is the number of objects, but with sampling, this method can still be used. We can take a sample of data points from each cluster, compute the similarity between these points, and plot the result. It may be necessary to oversample small clusters and undersample large ones to obtain an adequate representation of all clusters. 8.5.4 Unsupervised Evaluation of Hierarchical Clustering The previous approaches to cluster evaluation are intended for partitional clusterings. Here we discuss the cophenetic correlation, a popular evaluation measure for hierarchical clusterings. The cophenetic distance between two objects is the proximity at which an agglomerative hierarchical clustering tech- 8.5 1 Cluster Evaluation 545 1 1 0.9 10 0.9 0.8 20 0.8 20 0.8 30 0.7 30 0.7 30 0.7 40 0.6 40 0.6 40 0.6 50 0.5 50 0.5 50 0.5 60 0.4 60 0.4 60 0.4 70 0.3 70 0.3 70 0.3 80 0.2 80 0.2 80 0.2 90 0.1 90 0.1 90 100 20 40 60 80 100 0 Similarity 100 20 Points 40 60 80 100 0 Similarity Points 10 Points 0.9 20 Points 10 100 0.1 20 40 Points (a) Similarity matrix sorted by DBSCAN cluster labels. 60 80 100 0 Similarity Points (b) Similarity matrix sorted by K-means cluster labels. (c) Similarity matrix sorted by complete link cluster labels. Figure 8.31. Similarity matrices for clusters from random data. nique puts the objects in the same cluster for the ﬁrst time. For example, if at some point in the agglomerative hierarchical clustering process, the smallest distance between the two clusters that are merged is 0.1, then all points in one cluster have a cophenetic distance of 0.1 with respect to the points in the other cluster. In a cophenetic distance matrix, the entries are the cophenetic distances between each pair of objects. The cophenetic distance is diﬀerent for each hierarchical clustering of a set of points. Example 8.11 (Cophenetic Distance Matrix). Table 8.7 shows the cophentic distance matrix for the single link clustering shown in Figure 8.16. (The data for this ﬁgure consists of the 6 two-dimensional points given in Table 8.3.) Table 8.7. Cophenetic distance matrix for single link and data in table 8.3 Point P1 P2 P3 P4 P5 P6 P1 0 0.222 0.222 0.222 0.222 0.222 P2 0.222 0 0.148 0.151 0.139 0.148 P3 0.222 0.148 0 0.151 0.148 0.110 P4 0.222 0.151 0.151 0 0.151 0.151 P5 0.222 0.139 0.148 0.151 0 0.148 P6 0.222 0.148 0.110 0.151 0.148 0 The CoPhenetic Correlation Coeﬃcient (CPCC) is the correlation between the entries of this matrix and the original dissimilarity matrix and is 546 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms a standard measure of how well a hierarchical clustering (of a particular type) ﬁts the data. One of the most common uses of this measure is to evaluate which type of hierarchical clustering is best for a particular type of data. Example 8.12 (Cophenetic Correlation Coeﬃcient). We calculated the CPCC for the hierarchical clusterings shown in Figures 8.16–8.19.These values are shown in Table 8.8. The hierarchical clustering produced by the single link technique seems to ﬁt the data less well than the clusterings produced by complete link, group average, and Ward’s method. Table 8.8. Cophenetic correlation coefﬁcient for data of Table 8.3 and four agglomerative hierarchical clustering techniques. Technique Single Link Complete Link Group Average Ward’s 8.5.5 CPCC 0.44 0.63 0.66 0.64 Determining the Correct Number of Clusters Various unsupervised cluster evaluation measures can be used to approximately determine the correct or natural number of clusters. Example 8.13 (Number of Clusters). The data set of Figure 8.29 has 10 natural clusters. Figure 8.32 shows a plot of the SSE versus the number of clusters for a (bisecting) K-means clustering of the data set, while Figure 8.33 shows the average silhouette coeﬃcient versus the number of clusters for the same data. There is a distinct knee in the SSE and a distinct peak in the silhouette coeﬃcient when the number of clusters is equal to 10. Thus, we can try to ﬁnd the natural number of clusters in a data set by looking for the number of clusters at which there is a knee, peak, or dip in the plot of the evaluation measure when it is plotted against the number of clusters. Of course, such an approach does not always work well. Clusters may be considerably more intertwined or overlapping than those shown in Figure 8.29. Also, the data may consist of nested clusters. Actually, the clusters in Figure 8.29 are somewhat nested; i.e., there are 5 pairs of clusters since the clusters are closer top to bottom than they are left to right. There is a knee that indicates this in the SSE curve, but the silhouette coeﬃcient curve is not 8.5 10 Cluster Evaluation 547 0.75 0.7 8 Silhouette Coefficient 0.65 SSE 6 4 0.6 0.55 0.5 0.45 0.4 2 0.35 0 0 5 10 15 20 25 30 Number of Clusters Figure 8.32. SSE versus number of clusters for the data of Figure 8.29. 0.3 0 5 10 15 20 25 30 Number of Clusters Figure 8.33. Average silhouette coefﬁcient versus number of clusters for the data of Figure 8.29. as clear. In summary, while caution is needed, the technique we have just described can provide insight into the number of clusters in the data. 8.5.6 Clustering Tendency One obvious way to determine if a data set has clusters is to try to cluster it. However, almost all clustering algorithms will dutifully ﬁnd clusters when given data. To address this issue, we could evaluate the resulting clusters and only claim that a data set has clusters if at least some of the clusters are of good quality. However, this approach does not address the fact the clusters in the data can be of a diﬀerent type than those sought by our clustering algorithm. To handle this additional problem, we could use multiple algorithms and again evaluate the quality of the resulting clusters. If the clusters are uniformly poor, then this may indeed indicate that there are no clusters in the data. Alternatively, and this is the focus of measures of clustering tendency, we can try to evaluate whether a data set has clusters without clustering. The most common approach, especially for data in Euclidean space, has been to use statistical tests for spatial randomness. Unfortunately, choosing the correct model, estimating the parameters, and evaluating the statistical signiﬁcance of the hypothesis that the data is non-random can be quite challenging. Nonetheless, many approaches have been developed, most of them for points in low-dimensional Euclidean space. Example 8.14 (Hopkins Statistic). For this approach, we generate p points that are randomly distributed across the data space and also sample p actual 548 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms data points. For both sets of points we ﬁnd the distance to the nearest neighbor in the original data set. Let the ui be the nearest neighbor distances of the artiﬁcially generated points, while the wi are the nearest neighbor distances of the sample of points from the original data set. The Hopkins statistic H is then deﬁned by Equation 8.17. H= p i=1 wi p p i=1 ui + i=1 wi (8.17) If the randomly generated points and the sample of data points have roughly the same nearest neighbor distances, then H will be near 0.5. Values of H near 0 and 1 indicate, respectively, data that is highly clustered and data that is regularly distributed in the data space. To give an example, the Hopkins statistic for the data of Figure 8.26 was computed for p = 20 and 100 diﬀerent trials. The average value of H was 0.56 with a standard deviation of 0.03. The same experiment was performed for the well-separated points of Figure 8.30. The average value of H was 0.95 with a standard deviation of 0.006. 8.5.7 Supervised Measures of Cluster Validity When we have external information about data, it is typically in the form of externally derived class labels for the data objects. In such cases, the usual procedure is to measure the degree of correspondence between the cluster labels and the class labels. But why is this of interest? After all, if we have the class labels, then what is the point in performing a cluster analysis? Motivations for such an analysis are the comparison of clustering techniques with the “ground truth” or the evaluation of the extent to which a manual classiﬁcation process can be automatically produced by cluster analysis. We consider two diﬀerent kinds of approaches. The ﬁrst set of techniques use measures from classiﬁcation, such as entropy, purity, and the F-measure. These measures evaluate the extent to which a cluster contains objects of a single class. The second group of methods is related to the similarity measures for binary data, such as the Jaccard measure that we saw in Chapter 2. These approaches measure the extent to which two objects that are in the same class are in the same cluster and vice versa. For convenience, we will refer to these two types of measures as classiﬁcation-oriented and similarity-oriented, respectively. 8.5 Cluster Evaluation 549 Classiﬁcation-Oriented Measures of Cluster Validity There are a number of measures—entropy, purity, precision, recall, and the F-measure—that are commonly used to evaluate the performance of a classiﬁcation model. In the case of classiﬁcation, we measure the degree to which predicted class labels correspond to actual class labels, but for the measures just mentioned, nothing fundamental is changed by using cluster labels instead of predicted class labels. Next, we quickly review the deﬁnitions of these measures, which were discussed in Chapter 4. Entropy: The degree to which each cluster consists of objects of a single class. For each cluster, the class distribution of the data is calculated ﬁrst, i.e., for cluster j we compute pij , the probability that a member of cluster i belongs to class j as pij = mij /mi , where mi is the number of objects in cluster i and mij is the number of objects of class j in cluster i. Using this class distribution, the entropy of each cluster i is calculated using the standard formula, ei = − L=1 pij log2 pij , where L is the number of j classes. The total entropy for a set of clusters is calculated as the sum of the entropies of each cluster weighted by the size of each cluster, i.e., e = K mi ei , where K is the number of clusters and m is the total i=1 m number of data points. Purity: Another measure of the extent to which a cluster contains objects of a single class. Using the previous terminology, the purity of cluster i is pi = max pij , the overall purity of a clustering is purity = K mi pi . i=1 m j Precision: The fraction of a cluster that consists of objects of a speciﬁed class. The precision of cluster i with respect to class j is precision(i, j ) = pij . Recall: The extent to which a cluster contains all objects of a speciﬁed class. The recall of cluster i with respect to class j is recall(i, j ) = mij /mj , where mj is the number of objects in class j . F-measure A combination of both precision and recall that measures the extent to which a cluster contains only objects of a particular class and all objects of that class. The F-measure of cluster i with respect to class j is F (i, j ) = (2 × precision(i, j ) × recall(i, j ))/(precision(i, j )+ recall(i, j )). Example 8.15 (Supervised Evaluation Measures). We present an example to illustrate these measures. Speciﬁcally, we use K-means with the cosine similarity measure to cluster 3204 newspaper articles from the Los Angeles 550 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms Table 8.9. K-means clustering results for the LA Times document data set. Cluster 1 2 3 4 5 6 Total EnterFinancial tainment 3 5 4 7 1 1 10 162 331 22 5 358 354 555 Foreign Metro National Sports Entropy Purity 40 280 1 3 5 12 341 506 29 7 119 70 212 943 96 39 4 73 13 48 273 27 2 671 2 23 13 738 1.2270 1.1472 0.1813 1.7487 1.3976 1.5523 1.1450 0.7474 0.7756 0.9796 0.4390 0.7134 0.5525 0.7203 Times. These articles come from six diﬀerent classes: Entertainment, Financial, Foreign, Metro, National, and Sports. Table 8.9 shows the results of a K-means clustering to ﬁnd six clusters. The ﬁrst column indicates the cluster, while the next six columns together form the confusion matrix; i.e., these columns indicate how the documents of each category are distributed among the clusters. The last two columns are the entropy and purity of each cluster, respectively. Ideally, each cluster will contain documents from only one class. In reality, each cluster contains documents from many classes. Nevertheless, many clusters contain documents primarily from just one class. In particular, cluster 3, which contains mostly documents from the Sports section, is exceptionally good, both in terms of purity and entropy. The purity and entropy of the other clusters is not as good, but can typically be greatly improved if the data is partitioned into a larger number of clusters. Precision, recall, and the F-measure can be calculated for each cluster. To give a concrete example, we consider cluster 1 and the Metro class of Table 8.9. The precision is 506/677 = 0.75, recall is 506/943 = 0.26, and hence, the F value is 0.39. In contrast, the F value for cluster 3 and Sports is 0.94. Similarity-Oriented Measures of Cluster Validity The measures that we discuss in this section are all based on the premise that any two objects that are in the same cluster should be in the same class and vice versa. We can view this approach to cluster validity as involving the comparison of two matrices: (1) the ideal cluster similarity matrix discussed previously, which has a 1 in the ij th entry if two objects, i and j , are in the same cluster and 0, otherwise, and (2) an ideal class similarity matrix deﬁned with respect to class labels, which has a 1 in the ij th entry if 8.5 Cluster Evaluation 551 two objects, i and j , belong to the same class, and a 0 otherwise. As before, we can take the correlation of these two matrices as the measure of cluster validity. This measure is known as the Γ statistic in clustering validation literature. Example 8.16 (Correlation between Cluster and Class Matrices). To demonstrate this idea more concretely, we give an example involving ﬁve data points, p1 , p2 , p3 , p4 , p5 , two clusters, C1 = {p1 , p2 , p3 } and C2 = {p4 , p5 }, and two classes, L1 = {p1 , p2 } and L2 = {p3 , p4 , p5 }. The ideal cluster and class similarity matrices are given in Tables 8.10 and 8.11. The correlation between the entries of these two matrices is 0.359. Table 8.10. Ideal cluster similarity matrix. Point p1 p2 p3 p4 p5 p1 1 1 1 0 0 p2 1 1 1 0 0 p3 1 1 1 0 0 p4 0 0 0 1 1 p5 0 0 0 1 1 Table 8.11. Ideal class similarity matrix. Point p1 p2 p3 p4 p5 p1 1 1 0 0 0 p2 1 1 0 0 0 p3 0 0 1 1 1 p4 0 0 1 1 1 p5 0 0 1 1 1 More generally, we can use any of the measures for binary similarity that we saw in Section 2.4.5. (For example, we can convert these two matrices into binary vectors by appending the rows.) We repeat the deﬁnitions of the four quantities used to deﬁne those similarity measures, but modify our descriptive text to ﬁt the current context. Speciﬁcally, we need to compute the following four quantities for all pairs of distinct objects. (There are m(m − 1)/2 such pairs, if m is the number of objects.) f00 f01 f10 f11 = number of pairs of objects having a diﬀerent class and a diﬀerent cluster = number of pairs of objects having a diﬀerent class and the same cluster = number of pairs of objects having the same class and a diﬀerent cluster = number of pairs of objects having the same class and the same cluster In particular, the simple matching coeﬃcient, which is known as the Rand statistic in this context, and the Jaccard coeﬃcient are two of the most frequently used cluster validity measures. Rand statistic = f00 + f11 f00 + f01 + f10 + f11 (8.18) 552 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms Jaccard coeﬃcient = f11 f01 + f10 + f11 (8.19) Example 8.17 (Rand and Jaccard Measures). Based on these formulas, we can readily compute the Rand statistic and Jaccard coeﬃcient for the example based on Tables 8.10 and 8.11. Noting that f00 = 4, f01 = 2, f10 = 2, and f11 = 2, the Rand statistic = (2 + 4)/10 = 0.6 and the Jaccard coeﬃcient = 2/(2+2+2 ) = 0.33. We also note that the four quantities, f00 , f01 , f10 , and f11 , deﬁne a contingency table as shown in Table 8.12. Table 8.12. Two-way contingency table for determining whether pairs of objects are in the same class and same cluster. Same Cluster Diﬀerent Cluster Same Class f11 f10 Diﬀerent Class f01 f00 Previously, in the context of association analysis—see Section 6.7.1—we presented an extensive discussion of measures of association that can be used for this type of contingency table. (Compare Table 8.12 with Table 6.7.) Those measures can also be applied to cluster validity. Cluster Validity for Hierarchical Clusterings So far in this section, we have discussed supervised measures of cluster validity only for partitional clusterings. Supervised evaluation of a hierarchical clustering is more diﬃcult for a variety of reasons, including the fact that a preexisting hierarchical structure often does not exist. Here, we will give an example of an approach for evaluating a hierarchical clustering in terms of a (ﬂat) set of class labels, which are more likely to be available than a preexisting hierarchical structure. The key idea of this approach is to evaluate whether a hierarchical clustering contains, for each class, at least one cluster that is relatively pure and includes most of the objects of that class. To evaluate a hierarchical clustering with respect to this goal, we compute, for each class, the F-measure for each cluster in the cluster hierarchy. For each class, we take the maximum Fmeasure attained for any cluster. Finally, we calculate an overall F-measure for the hierarchical clustering by computing the weighted average of all per-class F-measures, where the weights are based on the class sizes. More formally, 8.5 Cluster Evaluation 553 this hierarchical F-measure is deﬁned as follows: F= j mj max F (i, j ) mi where the maximum is taken over all clusters i at all levels, mj is the number of objects in class j , and m is the total number of objects. 8.5.8 Assessing the Signiﬁcance of Cluster Validity Measures Cluster validity measures are intended to help us measure the goodness of the clusters that we have obtained. Indeed, they typically give us a single number as a measure of that goodness. However, we are then faced with the problem of interpreting the signiﬁcance of this number, a task that may be even more diﬃcult. The minimum and maximum values of cluster evaluation measures may provide some guidance in many cases. For instance, by deﬁnition, a purity of 0 is bad, while a purity of 1 is good, at least if we trust our class labels and want our cluster structure to reﬂect the class structure. Likewise, an entropy of 0 is good, as is an SSE of 0. Sometimes, however, there may not be a minimum or maximum value, or the scale of the data may aﬀect the interpretation. Also, even if there are minimum and maximum values with obvious interpretations, intermediate values still need to be interpreted. In some cases, we can use an absolute standard. If, for example, we are clustering for utility, we may be willing to tolerate only a certain level of error in the approximation of our points by a cluster centroid. But if this is not the case, then we must do something else. A common approach is to interpret the value of our validity measure in statistical terms. Speciﬁcally, we attempt to judge how likely it is that our observed value may be achieved by random chance. The value is good if it is unusual; i.e., if it is unlikely to be the result of random chance. The motivation for this approach is that we are only interested in clusters that reﬂect non-random structure in the data, and such structures should generate unusually high (low) values of our cluster validity measure, at least if the validity measures are designed to reﬂect the presence of strong cluster structure. Example 8.18 (Signiﬁcance of SSE). To show how this works, we present an example based on K-means and the SSE. Suppose that we want a measure of how good the well-separated clusters of Figure 8.30 are with respect to random data. We generate many random sets of 100 points having the same range as 554 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 50 40 Count 30 20 10 0 0.015 0.02 0.025 0.03 0.035 0.04 SSE Figure 8.34. Histogram of SSE for 500 random data sets. the points in the three clusters, ﬁnd three clusters in each data set using Kmeans, and accumulate the distribution of SSE values for these clusterings. By using this distribution of the SSE values, we can then estimate the probability of the SSE value for the original clusters. Figure 8.34 shows the histogram of the SSE from 500 random runs. The lowest SSE shown in Figure 8.34 is 0.0173. For the three clusters of Figure 8.30, the SSE is 0.0050. We could therefore conservatively claim that there is less than a 1% chance that a clustering such as that of Figure 8.30 could occur by chance. To conclude, we stress that there is more to cluster evaluation—supervised or unsupervised—than obtaining a numerical measure of cluster validity. Unless this value has a natural interpretation based on the deﬁnition of the measure, we need to interpret this value in some way. If our cluster evaluation measure is deﬁned such that lower values indicate stronger clusters, then we can use statistics to evaluate whether the value we have obtained is unusually low, provided we have a distribution for the evaluation measure. We have presented an example of how to ﬁnd such a distribution, but there is considerably more to this topic, and we refer the reader to the bibliographic notes for more pointers. Finally, even when an evaluation measure is used as a relative measure, i.e., to compare two clusterings, we still need to assess the signiﬁcance in the diﬀerence between the evaluation measures of the two clusterings. Although one value will almost always be better than another, it can be diﬃcult to determine if the diﬀerence is signiﬁcant. Note that there are two aspects to this signiﬁcance: whether the diﬀerence is statistically signiﬁcant (repeatable) 8.6 Bibliographic Notes 555 and whether the magnitude of the diﬀerence is meaningful with respect to the application. Many would not regard a diﬀerence of 0.1% as signiﬁcant, even if it is consistently reproducible. 8.6 Bibliographic Notes Discussion in this chapter has been most heavily inﬂuenced by the books on cluster analysis written by Jain and Dubes [396], Anderberg [374], and Kaufman and Rousseeuw [400]. Additional clustering books that may also be of interest include those by Aldenderfer and Blashﬁeld [373], Everitt et al. [388], Hartigan [394], Mirkin [405], Murtagh [407], Romesburg [409], and Sp¨th [413]. a A more statistically oriented approach to clustering is given by the pattern recognition book of Duda et al. [385], the machine learning book of Mitchell [406], and the book on statistical learning by Hastie et al. [395]. A general survey of clustering is given by Jain et al. [397], while a survey of spatial data mining techniques is provided by Han et al. [393]. Behrkin [379] provides a survey of clustering techniques for data mining. A good source of references to clustering outside of the data mining ﬁeld is the article by Arabie and Hubert [376]. A paper by Kleinberg [401] provides a discussion of some of the trade-oﬀs that clustering algorithms make and proves that it is impossible to for a clustering algorithm to simultaneously possess three simple properties. The K-means algorithm has a long history, but is still the subject of current research. The original K-means algorithm was proposed by MacQueen [403]. The ISODATA algorithm by Ball and Hall [377] was an early, but sophisticated version of K-means that employed various pre- and postprocessing techniques to improve on the basic algorithm. The K-means algorithm and many of its variations are described in detail in the books by Anderberg [374] and Jain and Dubes [396]. The bisecting K-means algorithm discussed in this chapter was described in a paper by Steinbach et al. [414], and an implementation of this and other clustering approaches is freely available for academic use in the CLUTO (CLUstering TOolkit) package created by Karypis [382]. Boley [380] has created a divisive partitioning clustering algorithm (PDDP) based on ﬁnding the ﬁrst principal direction (component) of the data, and Savaresi and Boley [411] have explored its relationship to bisecting K-means. Recent variations of K-means are a new incremental version of K-means (Dhillon et al. [383]), X-means (Pelleg and Moore [408]), and K-harmonic means (Zhang et al [416]). Hamerly and Elkan [392] discuss some clustering algorithms that produce better results than K-means. While some of the previously mentioned approaches address the initialization problem of K-means in some manner, 556 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms other approaches to improving K-means initialization can also be found in the work of Bradley and Fayyad [381]. Dhillon and Modha [384] present a generalization of K-means, called spherical K-means, that works with commonly used similarity functions. A general framework for K-means clustering that uses dissimilarity functions based on Bregman divergences was constructed by Banerjee et al. [378]. Hierarchical clustering techniques also have a long history. Much of the initial activity was in the area of taxonomy and is covered in books by Jardine and Sibson [398] and Sneath and Sokal [412]. General-purpose discussions of hierarchical clustering are also available in most of the clustering books mentioned above. Agglomerative hierarchical clustering is the focus of most work in the area of hierarchical clustering, but divisive approaches have also received some attention. For example, Zahn [415] describes a divisive hierarchical technique that uses the minimum spanning tree of a graph. While both divisive and agglomerative approaches typically take the view that merging (splitting) decisions are ﬁnal, there has been some work by Fisher [389] and Karypis et al. [399] to overcome these limitations. Ester et al. proposed DBSCAN [387], which was later generalized to the GDBSCAN algorithm by Sander et al. [410] in order to handle more general types of data and distance measures, such as polygons whose closeness is measured by the degree of intersection. An incremental version of DBSCAN was developed by Kriegel et al. [386]. One interesting outgrowth of DBSCAN is OPTICS (Ordering Points To Identify the Clustering Structure) (Ankerst et al. [375]), which allows the visualization of cluster structure and can also be used for hierarchical clustering. An authoritative discussion of cluster validity, which strongly inﬂuenced the discussion in this chapter, is provided in Chapter 4 of Jain and Dubes’ clustering book [396]. More recent reviews of cluster validity are those of Halkidi et al. [390, 391] and Milligan [404]. Silhouette coeﬃcients are described in Kaufman and Rousseeuw’s clustering book [400]. The source of the cohesion and separation measures in Table 8.6 is a paper by Zhao and Karypis [417], which also contains a discussion of entropy, purity, and the hierarchical Fmeasure. The original source of the hierarchical F-measure is an article by Larsen and Aone [402]. Bibliography [373] M. S. Aldenderfer and R. K. Blashﬁeld. Cluster Analysis. Sage Publications, Los Angeles, 1985. [374] M. R. Anderberg. Cluster Analysis for Applications. Academic Press, New York, December 1973. Bibliography 557 [375] M. Ankerst, M. M. Breunig, H.-P. Kriegel, and J. Sander. OPTICS: Ordering Points To Identify the Clustering Structure. In Proc. of 1999 ACM-SIGMOD Intl. Conf. on Management of Data, pages 49–60, Philadelphia, Pennsylvania, June 1999. ACM Press. [376] P. Arabie, L. Hubert, and G. D. Soete. An overview of combinatorial data analysis. In P. Arabie, L. Hubert, and G. D. Soete, editors, Clustering and Classiﬁcation, pages 188–217. World Scientiﬁc, Singapore, January 1996. [377] G. Ball and D. Hall. A Clustering Technique for Summarizing Multivariate Data. Behavior Science, 12:153–155, March 1967. [378] A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with Bregman Divergences. In Proc. of the 2004 SIAM Intl. Conf. on Data Mining, pages 234–245, Lake Buena Vista, FL, April 2004. [379] P. Berkhin. Survey Of Clustering Data Mining Techniques. Technical report, Accrue Software, San Jose, CA, 2002. [380] D. Boley. Principal Direction Divisive Partitioning. Data Mining and Knowledge Discovery, 2(4):325–344, 1998. [381] P. S. Bradley and U. M. Fayyad. Reﬁning Initial Points for K-Means Clustering. In Proc. of the 15th Intl. Conf. on Machine Learning, pages 91–99, Madison, WI, July 1998. Morgan Kaufmann Publishers Inc. [382] CLUTO 2.1.1: Software for Clustering High-Dimensional Datasets. /www.cs.umn.edu/∼karypis, November 2003. [383] I. S. Dhillon, Y. Guan, and J. Kogan. Iterative Clustering of High Dimensional Text Data Augmented by Local Search. In Proc. of the 2002 IEEE Intl. Conf. on Data Mining, pages 131–138. IEEE Computer Society, 2002. [384] I. S. Dhillon and D. S. Modha. Concept Decompositions for Large Sparse Text Data Using Clustering. Machine Learning, 42(1/2):143–175, 2001. [385] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classiﬁcation. John Wiley & Sons, Inc., New York, second edition, 2001. [386] M. Ester, H.-P. Kriegel, J. Sander, M. Wimmer, and X. Xu. Incremental Clustering for Mining in a Data Warehousing Environment. In Proc. of the 24th VLDB Conf., pages 323–333, New York City, August 1998. Morgan Kaufmann. [387] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. of the 2nd Intl. Conf. on Knowledge Discovery and Data Mining, pages 226–231, Portland, Oregon, August 1996. AAAI Press. [388] B. S. Everitt, S. Landau, and M. Leese. Cluster Analysis. Arnold Publishers, London, fourth edition, May 2001. [389] D. Fisher. Iterative Optimization and Simpliﬁcation of Hierarchical Clusterings. Journal of Artiﬁcial Intelligence Research, 4:147–179, 1996. [390] M. Halkidi, Y. Batistakis, and M. Vazirgiannis. Cluster validity methods: part I. SIGMOD Record (ACM Special Interest Group on Management of Data), 31(2):40–45, June 2002. [391] M. Halkidi, Y. Batistakis, and M. Vazirgiannis. Clustering validity checking methods: part II. SIGMOD Record (ACM Special Interest Group on Management of Data), 31 (3):19–27, Sept. 2002. [392] G. Hamerly and C. Elkan. Alternatives to the k-means algorithm that ﬁnd better clusterings. In Proc. of the 11th Intl. Conf. on Information and Knowledge Management, pages 600–607, McLean, Virginia, 2002. ACM Press. 558 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms [393] J. Han, M. Kamber, and A. Tung. Spatial Clustering Methods in Data Mining: A review. In H. J. Miller and J. Han, editors, Geographic Data Mining and Knowledge Discovery, pages 188–217. Taylor and Francis, London, December 2001. [394] J. Hartigan. Clustering Algorithms. Wiley, New York, 1975. [395] T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning: Data Mining, Inference, Prediction. Springer, New York, 2001. [396] A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall Advanced Reference Series. Prentice Hall, March 1988. Book available online at http://www.cse.msu.edu/∼jain/Clustering Jain Dubes.pdf. [397] A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):264–323, September 1999. [398] N. Jardine and R. Sibson. Mathematical Taxonomy. Wiley, New York, 1971. [399] G. Karypis, E.-H. Han, and V. Kumar. Multilevel Reﬁnement for Hierarchical Clustering. Technical Report TR 99-020, University of Minnesota, Minneapolis, MN, 1999. [400] L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics. John Wiley and Sons, New York, November 1990. [401] J. M. Kleinberg. An Impossibility Theorem for Clustering. In Proc. of the 16th Annual Conf. on Neural Information Processing Systems, December, 9–14 2002. [402] B. Larsen and C. Aone. Fast and Eﬀective Text Mining Using Linear-Time Document Clustering. In Proc. of the 5th Intl. Conf. on Knowledge Discovery and Data Mining, pages 16–22, San Diego, California, 1999. ACM Press. [403] J. MacQueen. Some methods for classiﬁcation and analysis of multivariate observations. In Proc. of the 5th Berkeley Symp. on Mathematical Statistics and Probability, pages 281–297. University of California Press, 1967. [404] G. W. Milligan. Clustering Validation: Results and Implications for Applied Analyses. In P. Arabie, L. Hubert, and G. D. Soete, editors, Clustering and Classiﬁcation, pages 345–375. World Scientiﬁc, Singapore, January 1996. [405] B. Mirkin. Mathematical Classiﬁcation and Clustering, volume 11 of Nonconvex Optimization and Its Applications. Kluwer Academic Publishers, August 1996. [406] T. Mitchell. Machine Learning. McGraw-Hill, Boston, MA, 1997. [407] F. Murtagh. Multidimensional Clustering Algorithms. Physica-Verlag, Heidelberg and Vienna, 1985. [408] D. Pelleg and A. W. Moore. X -means: Extending K -means with Eﬃcient Estimation of the Number of Clusters. In Proc. of the 17th Intl. Conf. on Machine Learning, pages 727–734. Morgan Kaufmann, San Francisco, CA, 2000. [409] C. Romesburg. Cluster Analysis for Researchers. Life Time Learning, Belmont, CA, 1984. [410] J. Sander, M. Ester, H.-P. Kriegel, and X. Xu. Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications. Data Mining and Knowledge Discovery, 2(2):169–194, 1998. [411] S. M. Savaresi and D. Boley. A comparative analysis on the bisecting K-means and the PDDP clustering algorithms. Intelligent Data Analysis, 8(4):345–362, 2004. [412] P. H. A. Sneath and R. R. Sokal. Numerical Taxonomy. Freeman, San Francisco, 1971. [413] H. Sp¨th. Cluster Analysis Algorithms for Data Reduction and Classiﬁcation of Oba jects, volume 4 of Computers and Their Application. Ellis Horwood Publishers, Chichester, 1980. ISBN 0-85312-141-9. 8.7 Exercises 559 [414] M. Steinbach, G. Karypis, and V. Kumar. A Comparison of Document Clustering Techniques. In Proc. of KDD Workshop on Text Mining, Proc. of the 6th Intl. Conf. on Knowledge Discovery and Data Mining, Boston, MA, August 2000. [415] C. T. Zahn. Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters. IEEE Transactions on Computers, C-20(1):68–86, Jan. 1971. [416] B. Zhang, M. Hsu, and U. Dayal. K-Harmonic Means—A Data Clustering Algorithm. Technical Report HPL-1999-124, Hewlett Packard Laboratories, Oct. 29 1999. [417] Y. Zhao and G. Karypis. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 55(3):311–331, 2004. 8.7 Exercises 1. Consider a data set consisting of 220 data vectors, where each vector has 32 components and each component is a 4-byte value. Suppose that vector quantization is used for compression and that 216 prototype vectors are used. How many bytes of storage does that data set take before and after compression and what is the compression ratio? 2. Find all well-separated clusters in the set of points shown in Figure 8.35. Figure 8.35. Points for Exercise 2. 3. Many partitional clustering algorithms that automatically determine the number of clusters claim that this is an advantage. List two situations in which this is not the case. 4. Given K equally sized clusters, the probability that a randomly chosen initial centroid will come from any given cluster is 1/K , but the probability that each cluster will have exactly one initial centroid is much lower. (It should be clear that having one initial centroid in each cluster is a good starting situation for K-means.) In general, if there are K clusters and each cluster has n points, then the probability, p, of selecting in a sample of size K one initial centroid from each cluster is given by Equation 8.20. (This assumes sampling with replacement.) From this formula we can calculate, for example, that the chance of having one initial centroid from each of four clusters is 4!/44 = 0.0938. 560 Chapter 8 p= Cluster Analysis: Basic Concepts and Algorithms number of ways to select one centroid from each cluster K !nK K! = =K number of ways to select K centroids (Kn)K K (8.20) (a) Plot the probability of obtaining one point from each cluster in a sample of size K for values of K between 2 and 100. (b) For K clusters, K = 10, 100, and 1000, ﬁnd the probability that a sample of size 2K contains at least one point from each cluster. You can use either mathematical methods or statistical simulation to determine the answer. 5. Identify the clusters in Figure 8.36 using the center-, contiguity-, and densitybased deﬁnitions. Also indicate the number of clusters for each case and give a brief indication of your reasoning. Note that darkness or the number of dots indicates density. If it helps, assume center-based means K-means, contiguitybased means single link, and density-based means DBSCAN. (a) (b) (c) (d) Figure 8.36. Clusters for Exercise 5. 6. For the following sets of two-dimensional points, (1) provide a sketch of how they would be split into clusters by K-means for the given number of clusters and (2) indicate approximately where the resulting centroids would be. Assume that we are using the squared error objective function. If you think that there is more than one possible solution, then please indicate whether each solution is a global or local minimum. Note that the label of each diagram in Figure 8.37 matches the corresponding part of this question, e.g., Figure 8.37(a) goes with part (a). (a) K = 2. Assuming that the points are uniformly distributed in the circle, how many possible ways are there (in theory) to partition the points into two clusters? What can you say about the positions of the two centroids? (Again, you don’t need to provide exact centroid locations, just a qualitative description.) 8.7 (a) (b) (c) (d) Exercises 561 (e) Figure 8.37. Diagrams for Exercise 6. (b) K = 3. The distance between the edges of the circles is slightly greater than the radii of the circles. (c) K = 3. The distance between the edges of the circles is much less than the radii of the circles. (d) K = 2. (e) K = 3. Hint: Use the symmetry of the situation and remember that we are looking for a rough sketch of what the result would be. 7. Suppose that for a data set • there are m points and K clusters, • half the points and clusters are in “more dense” regions, • half the points and clusters are in “less dense” regions, and • the two regions are well-separated from each other. For the given data set, which of the following should occur in order to minimize the squared error when ﬁnding K clusters: (a) Centroids should be equally distributed between more dense and less dense regions. (b) More centroids should be allocated to the less dense region. (c) More centroids should be allocated to the denser region. Note: Do not get distracted by special cases or bring in factors other than density. However, if you feel the true answer is diﬀerent from any given above, justify your response. 8. Consider the mean of a cluster of objects from a binary transaction data set. What are the minimum and maximum values of the components of the mean? What is the interpretation of components of the cluster mean? Which components most accurately characterize the objects in the cluster? 9. Give an example of a data set consisting of three natural clusters, for which (almost always) K-means would likely ﬁnd the correct clusters, but bisecting K-means would not. 562 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 10. Would the cosine measure be the appropriate similarity measure to use with Kmeans clustering for time series data? Why or why not? If not, what similarity measure would be more appropriate? 11. Total SSE is the sum of the SSE for each separate attribute. What does it mean if the SSE for one variable is low for all clusters? Low for just one cluster? High for all clusters? High for just one cluster? How could you use the per variable SSE information to improve your clustering? 12. The leader algorithm (Hartigan [394]) represents each cluster using a point, known as a leader, and assigns each point to the cluster corresponding to the closest leader, unless this distance is above a user-speciﬁed threshold. In that case, the point becomes the leader of a new cluster. (a) What are the advantages and disadvantages of the leader algorithm as compared to K-means? (b) Suggest ways in which the leader algorithm might be improved. 13. The Voronoi diagram for a set of K points in the plane is a partition of all the points of the plane into K regions, such that every point (of the plane) is assigned to the closest point among the K speciﬁed points. (See Figure 8.38.) What is the relationship between Voronoi diagrams and K-means clusters? What do Voronoi diagrams tell us about the possible shapes of K-means clusters? Figure 8.38. Voronoi diagram for Exercise 13. 14. You are given a data set with 100 records and are asked to cluster the data. You use K-means to cluster the data, but for all values of K , 1 ≤ K ≤ 100, the K-means algorithm returns only one non-empty cluster. You then apply an incremental version of K-means, but obtain exactly the same result. How is this possible? How would single link or DBSCAN handle such data? 15. Traditional agglomerative hierarchical clustering routines merge two clusters at each step. Does it seem likely that such an approach accurately captures the 8.7 Exercises 563 (nested) cluster structure of a set of data points? If not, explain how you might postprocess the data to obtain a more accurate view of the cluster structure. 16. Use the similarity matrix in Table 8.13 to perform single and complete link hierarchical clustering. Show your results by drawing a dendrogram. The dendrogram should clearly show the order in which the points are merged. Table 8.13. Similarity matrix for Exercise 16. p1 p2 p3 p4 p5 p1 1.00 0.10 0.41 0.55 0.35 p2 0.10 1.00 0.64 0.47 0.98 p3 0.41 0.64 1.00 0.44 0.85 p4 0.55 0.47 0.44 1.00 0.76 p5 0.35 0.98 0.85 0.76 1.00 17. Hierarchical clustering is sometimes used to generate K clusters, K > 1 by taking the clusters at the K th level of the dendrogram. (Root is at level 1.) By looking at the clusters produced in this way, we can evaluate the behavior of hierarchical clustering on diﬀerent types of data and clusters, and also compare hierarchical approaches to K-means. The following is a set of one-dimensional points: {6, 12, 18, 24, 30, 42, 48}. (a) For each of the following sets of initial centroids, create two clusters by assigning each point to the nearest centroid, and then calculate the total squared error for each set of two clusters. Show both the clusters and the total squared error for each set of centroids. i. {18, 45} ii. {15, 40} (b) Do both sets of centroids represent stable solutions; i.e., if the K-means algorithm was run on this set of points using the given centroids as the starting centroids, would there be any change in the clusters generated? (c) What are the two clusters produced by single link? (d) Which technique, K-means or single link, seems to produce the “most natural” clustering in this situation? (For K-means, take the clustering with the lowest squared error.) (e) What deﬁnition(s) of clustering does this natural clustering correspond to? (Well-separated, center-based, contiguous, or density.) (f) What well-known characteristic of the K-means algorithm explains the previous behavior? 564 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 18. Suppose we ﬁnd K clusters using Ward’s method, bisecting K-means, and ordinary K-means. Which of these solutions represents a local or global minimum? Explain. 19. Hierarchical clustering algorithms require O(m2 log(m)) time, and consequently, are impractical to use directly on larger data sets. One possible technique for reducing the time required is to sample the data set. For example, if K clusters √ are desired and m points are sampled from the m points, then a hierarchical clustering algorithm will produce a hierarchical clustering in roughly O(m) time. K clusters can be extracted from this hierarchical clustering by taking the clusters on the K th level of the dendrogram. The remaining points can then be assigned to a cluster in linear time, by using various strategies. To give a speciﬁc example, the centroids of the K clusters can be computed, and then √ each of the m − m remaining points can be assigned to the cluster associated with the closest centroid. For each of the following types of data or clusters, discuss brieﬂy if (1) sampling will cause problems for this approach and (2) what those problems are. Assume that the sampling technique randomly chooses points from the total set of m points and that any unmentioned characteristics of the data or clusters are as optimal as possible. In other words, focus only on problems caused by the particular characteristic mentioned. Finally, assume that K is very much less than m. (a) Data with very diﬀerent sized clusters. (b) High-dimensional data. (c) Data with outliers, i.e., atypical points. (d) Data with highly irregular regions. (e) Data with globular clusters. (f) Data with widely diﬀerent densities. (g) Data with a small percentage of noise points. (h) Non-Euclidean data. (i) Euclidean data. (j) Data with many and mixed attribute types. 20. Consider the following four faces shown in Figure 8.39. Again, darkness or number of dots represents density. Lines are used only to distinguish regions and do not represent points. (a) For each ﬁgure, could you use single link to ﬁnd the patterns represented by the nose, eyes, and mouth? Explain. (b) For each ﬁgure, could you use K-means to ﬁnd the patterns represented by the nose, eyes, and mouth? Explain. 8.7 (a) (b) Exercises 565 (c) (d) Figure 8.39. Figure for Exercise 20. (c) What limitation does clustering have in detecting all the patterns formed by the points in Figure 8.39(c)? 21. Compute the entropy and purity for the confusion matrix in Table 8.14. Cluster #1 #2 #3 Total Table 8.14. Confusion matrix for Exercise 21. Entertainment Financial Foreign Metro National 1 1 0 11 4 27 89 333 827 253 326 465 8 105 16 354 555 341 943 273 Sports 676 33 29 738 Total 693 1562 949 3204 22. You are given two sets of 100 points that fall within the unit square. One set of points is arranged so that the points are uniformly spaced. The other set of points is generated from a uniform distribution over the unit square. (a) Is there a diﬀerence between the two sets of points? (b) If so, which set of points will typically have a smaller SSE for K=10 clusters? (c) What will be the behavior of DBSCAN on the uniform data set? The random data set? 23. Using the data in Exercise 24, compute the silhouette coeﬃcient for each point, each of the two clusters, and the overall clustering. 24. Given the set of cluster labels and similarity matrix shown in Tables 8.15 and 8.16, respectively, compute the correlation between the similarity matrix and the ideal similarity matrix, i.e., the matrix whose ij th entry is 1 if two objects belong to the same cluster, and 0 otherwise. 566 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms Table 8.15. Table of cluster labels for Exercise 24. Table 8.16. Point Cluster Label Point P1 1 P1 P2 1 P2 P3 2 P3 P4 2 P4 Similarity matrix for Exercise 24. P1 1 0.8 0.65 0.55 P2 0.8 1 0.7 0.6 P3 0.65 0.7 1 0.9 P4 0.55 0.6 0.9 1 25. Compute the hierarchical F-measure for the eight objects {p1, p2, p3, p4, p5, p6, p7, p8} and hierarchical clustering shown in Figure 8.40. Class A contains points p1, p2, and p3, while p4, p5, p6, p7, and p8 belong to class B. {p1, p2, p3, p4, p5, p6, p7, p8} {p1, p2, p4, p5,} {p1, p2} {p4, p5} {p3, p6, p7, p8} {p3, p6} {p7, p8} Figure 8.40. Hierarchical clustering for Exercise 25. 26. Compute the cophenetic correlation coeﬃcient for the hierarchical clusterings in Exercise 16. (You will need to convert the similarities into dissimilarities.) 27. Prove Equation 8.14. 28. Prove Equation 8.16. K 29. Prove that i=1 x∈Ci (x − mi )(m − mi ) = 0. This fact was used in the proof that TSS = SSE + SSB in Section 8.5.2. 30. Clusters of documents can be summarized by ﬁnding the top terms (words) for the documents in the cluster, e.g., by taking the most frequent k terms, where k is a constant, say 10, or by taking all terms that occur more frequently than a speciﬁed threshold. Suppose that K-means is used to ﬁnd clusters of both documents and words for a document data set. (a) How might a set of term clusters deﬁned by the top terms in a document cluster diﬀer from the word clusters found by clustering the terms with K-means? (b) How could term clustering be used to deﬁne clusters of documents? 31. We can represent a data set as a collection of object nodes and a collection of attribute nodes, where there is a link between each object and each attribute, 8.7 Exercises 567 and where the weight of that link is the value of the object for that attribute. For sparse data, if the value is 0, the link is omitted. Bipartite clustering attempts to partition this graph into disjoint clusters, where each cluster consists of a set of object nodes and a set of attribute nodes. The objective is to maximize the weight of links between the object and attribute nodes of a cluster, while minimizing the weight of links between object and attribute links in diﬀerent clusters. This type of clustering is also known as co-clustering since the objects and attributes are clustered at the same time. (a) How is bipartite clustering (co-clustering) diﬀerent from clustering the sets of objects and attributes separately? (b) Are there any cases in which these approaches yield the same clusters? (c) What are the strengths and weaknesses of co-clustering as compared to ordinary clustering? 32. In Figure 8.41, match the similarity matrices, which are sorted according to cluster labels, with the sets of points. Diﬀerences in shading and marker shape distinguish between clusters, and each set of points contains 100 points and three clusters. In the set of points labeled 2, there are three very tight, equalsized clusters. 568 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms 1 1 0.8 0.7 0.7 0.6 0.6 0.5 0.5 y 0.9 0.8 y 2 1 0.9 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0 0 0.1 0.2 0.4 0.6 0.8 0 0 1 0.2 0.4 x 3 0.8 1 0.6 0.8 1 4 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 y 1 y 0.6 x 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0.2 0.4 0.6 x 0.8 1 0 0 0.2 0.4 x Figure 8.41. Points and similarity matrices for Exercise 32. ...
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