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Unformatted text preview: Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Data Mining Clustering: Advanced Concepts Lecture Notes 18 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2 Hierarchical Clustering: Revisited Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed u MIN (single link): susceptible to noise/outliers u MAX/GROUP AVERAGE: may not work well with nonglobular clusters CURE algorithm tries to handle both problems Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 3 Uses a number of points to represent a cluster Representative points are found by selecting a constant number of points from a cluster and then shrinking them toward the center of the cluster Cluster similarity is the similarity of the closest pair of representative points from different clusters CURE: Another Hierarchical Approach &#2; &#2; Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 4 CURE Shrinking representative points toward the center helps avoid problems with noise and outliers CURE is better able to handle clusters of arbitrary shapes and sizes Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 5 Experimental Results: C URE Picture from CURE , Guha, Rastogi, Shim. Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 6 Experimental Results: CURE Picture from CURE , Guha, Rastogi, Shim. (centroid) (single link) Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 7 CURE Cannot Handle Differing Densities Original Points CURE Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 8 GraphBased Clustering GraphBased clustering uses the proximity graph Start with the proximity matrix Consider each point as a node in a graph Each edge between two nodes has a weight which is the proximity between the two points Initially the proximity graph is fully connected Tan,Steinbach, Kumar...
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This note was uploaded on 04/08/2010 for the course CS 420 taught by Professor Dawsonengler during the Spring '02 term at San Jose State University .
 Spring '02
 dawsonengler
 Algorithms, Data Mining

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