lect4 - Clustering Lecture outline Distance/Similarity...

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Clustering
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Lecture outline Distance/Similarity between data objects Data objects as geometric data points Clustering problems and algorithms K-means K-median K-center
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What is clustering? A grouping of data objects such that the objects within a group are similar (or related) to one another and different from (or unrelated to) the objects in other groups Inter-cluster  distances are  maximized Intra-cluster  distances are  minimized
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Outliers Outliers are objects that do not belong to any cluster or form clusters of very small cardinality In some applications we are interested in discovering outliers, not clusters ( outlier analysis ) cluster outliers
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Why do we cluster? Clustering : given a collection of data objects group them so that Similar to one another within the same cluster Dissimilar to the objects in other clusters Clustering results are used: As a stand-alone tool to get insight into data distribution Visualization of clusters may unveil important information As a preprocessing step for other algorithms Efficient indexing or compression often relies on clustering
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Applications of clustering? Image Processing cluster images based on their visual content Web Cluster groups of users based on their access patterns on webpages Cluster webpages based on their content Bioinformatics Cluster similar proteins together (similarity wrt chemical structure and/or functionality etc) Many more…
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The clustering task Group observations into groups so that the observations belonging in the same group are similar, whereas observations in different groups are different Basic questions: What does “similar” mean What is a good partition of the objects? I.e., how is the quality of a solution measured How to find a good partition of the observations
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Observations to cluster Real-value attributes/variables e.g., salary, height Binary attributes e.g., gender (M/F), has_cancer(T/F) Nominal (categorical) attributes e.g., religion (Christian, Muslim, Buddhist, Hindu, etc.) Ordinal/Ranked attributes e.g., military rank (soldier, sergeant, lutenant, captain, etc.) Variables of mixed types multiple attributes with various types
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Observations to cluster Usually data objects consist of a set of attributes (also known as dimensions ) J. Smith, 20, 200K If all d dimensions are real-valued then we can visualize each data point as points in a d -
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