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Unformatted text preview: 1 CSE 572: Data Mining Lecture 16: Hierarchical Clustering 2 Hierarchical Clustering Produces a set of nested clusters organized as a hierarchical tree Can be visualized as a dendrogram A tree like diagram that records the sequences of merges or splits 1 3 2 5 4 6 0.05 0.1 0.15 0.2 1 2 3 4 5 6 1 2 3 4 5 3 Strengths of Hierarchical Clustering Do not have to assume any particular number of clusters Any desired number of clusters can be obtained by cutting the dendrogram at the proper level They may correspond to meaningful taxonomies Example in biological sciences (e.g., animal kingdom, phylogeny reconstruction, ) 4 Hierarchical Clustering Two main types of hierarchical clustering Agglomerative: Start with the points as individual clusters At each step, merge the closest pair of clusters until only one cluster (or k clusters) left Divisive: Start with one, allinclusive cluster At each step, split a cluster until each cluster contains a point (or there are k clusters) Traditional hierarchical algorithms use a similarity or distance matrix Merge or split one cluster at a time 5 MST: Divisive Hierarchical Clustering Build MST (Minimum Spanning Tree) Start with a tree that consists of any point In successive steps, look for the closest pair of points (p, q) such that one point (p) is in the current tree but the other (q) is not Add q to the tree and put an edge between p and q 6 MST: Divisive Hierarchical Clustering Use MST for constructing hierarchy of clusters 7 Agglomerative Clustering Algorithm More popular hierarchical clustering technique Basic algorithm is straightforward 1. Compute the proximity matrix 2. Let each data point be a cluster 3. Repeat 1. Merge the two closest clusters 2. Update the proximity matrix 1. Until only a single cluster remains...
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 Spring '02
 dawsonengler

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