Data Miningcluster Analysis Advanced Conceptsand Algorithmsl

Data Miningcluster Analysis Advanced Conceptsand Algorithmslecture No 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 4/18/2004 * (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 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 (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 CURE: Another Hierarchical Approach 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 ï‚´ ï‚´ (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 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 (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 Experimental Results: CURE Picture from CURE, Guha, Rastogi, Shim. (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 Experimental Results: CURE Picture from CURE, Guha, Rastogi, Shim. (centroid) (single link) (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 CURE Cannot Handle Differing Densities Original Points CURE (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR .bin 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. (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 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 drastically reduced The size of the problems that can be handled is increased (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 Graph-Based Clustering: Sparsification … Clustering may work better Sparsification techniques keep the connections to the most similar (nearest) neighbors of a point while breaking the connections to less similar points.