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Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining

Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed MIN (single link): susceptible to noise/outliers MAX/GROUP AVERAGE: may not work well with non-globular clusters CURE algorithm tries to handle both problems Often starts with a proximity matrix A type of graph-based algorithm | Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining Hierarchical Clustering: Revisited Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed MIN (single link): susceptible to noise/outliers MAX/GROUP AVERAGE: may not work well with non-globular clusters CURE algorithm tries to handle both problems Often starts with a proximity matrix A type of graph-based algorithm 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 CURE Shrinking representative points toward the center helps avoid . | Data Mining Cluster Analysis: Advanced Concepts and Algorithms Lecture Notes for Chapter 9 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining Hierarchical Clustering: Revisited Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed MIN (single link): susceptible to noise/outliers MAX/GROUP AVERAGE: may not work well with non-globular clusters CURE algorithm tries to handle both problems Often starts with a proximity matrix A type of graph-based algorithm 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 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 Experimental Results: CURE Picture from CURE, Guha, Rastogi, Shim. Experimental Results: CURE Picture from CURE, Guha, Rastogi, Shim. (centroid) (single link) CURE Cannot Handle Differing Densities Original Points CURE Graph-Based Clustering Graph-Based 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 MIN (single-link) and MAX (complete-link) can be viewed as starting with this graph In the simplest case, clusters are connected components in the graph. Graph-Based Clustering: Sparsification The amount of data that needs to be processed is drastically reduced Sparsification can eliminate more than 99% of the entries in a proximity matrix The amount of time required to cluster the data is .