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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 23 Clustering Definition Given a set of data points, each having a set of attributes, and a similarity measure among them, find clusters such that Data points in one cluster are more similar to one another. Data points in separate clusters are less similar to one another. Similarity Measures: Euclidean Distance if attributes are continuous. Other Problem-specific Measures.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 24 Illustrating Clustering Euclidean Distance Based Clustering in 3-D space. Intracluster distances are minimized Intracluster distances are minimized Intercluster distances are maximized Intercluster distances are maximized

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 25 Clustering: Application 1 Market Segmentation: Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. Approach: Collect different attributes of customers based on their geographical and lifestyle related information. Find clusters of similar customers. Measure the clustering quality by observing buying patterns of customers in same cluster vs. those from different clusters.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 26 Clustering: Application 2 Document Clustering: Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. Approach: To identify frequently occurring terms in each document. Form a similarity measure based on the frequencies of different terms. Use it to cluster. Gain: Information Retrieval can utilize the clusters to relate a new document or search term to clustered documents.

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 27 Illustrating Document Clustering Clustering Points: 3204 Articles of Los Angeles Times. Similarity Measure: How many words are common in these documents (after some word filtering). Category Total Articles Correctly Placed Financial 555 364 Foreign 341 260 National 273 36 Metro 943 746 Sports 738 573 Entertainment 354 278
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 28 Clustering of S&P 500 Stock Data Discovered Clusters Industry Group 1 Applied-Matl-DOWN,Bay-Network-Down,3-COM-DOWN, Cabletron-Sys-DOWN,CISCO-DOWN,HP-DOWN, DSC-Comm-DOWN,INTEL-DOWN,LSI-Logic-DOWN, Micron-Tech-DOWN,Texas-Inst-Down,Tellabs-Inc-Down, Natl-Semiconduct-DOWN,Oracl-DOWN,SGI-DOWN, Sun-DOWN Technology1-DOWN 2 Apple-Comp-DOWN,Autodesk-DOWN,DEC-DOWN, ADV-Micro-Device-DOWN,Andrew-Corp-DOWN, Computer-Assoc-DOWN,Circuit-City-DOWN, Compaq-DOWN, EMC-Corp-DOWN, Gen-Inst-DOWN, Motorola-DOWN,Microsoft-DOWN,Scientific-Atl-DOWN Technology2-DOWN 3 Fannie-Mae-DOWN,Fed-Home-Loan-DOWN, MBNA-Corp-DOWN,Morgan-Stanley-DOWN Financial-DOWN 4 Baker-Hughes-UP,Dresser-Inds-UP,Halliburton-HLD-UP, Louisiana-Land-UP,Phillips-Petro-UP,Unocal-UP, Schlumberger-UP

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