The University of Adelaide, School of Computer Science 9 November 2018 Data Mining: Concepts and Techniques Chapter 10 Cluster Analysis: Basic Concepts and Methods Copyright © 2012, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.1 Overview of clustering methods discussed in this chapter. Note that some algorithms may combine various methods. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.2 The k-means partitioning algorithm. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.3 Clustering of a set of objects using the k-means method; for (b) update cluster centers and reassign objects accordingly (the mean of each cluster is marked by a +). Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.4 Four cases of the cost function for k-medoids clustering. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.5 PAM, a k-medoids partitioning algorithm. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.6 Agglomerative and divisive hierarchical clustering on data objects {a,b,c,d,e}. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.7 Dendrogram representation for hierarchical clustering of data objects {a,b,c,d,e}. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.8 Hierarchical clustering using single and complete linkages. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.9 CF-tree structure. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.10 Chameleon: hierarchical clustering based on k-nearest neighbors and dynamic modeling. Source: Based on Karypis, Han, and Kumar [KHK99]. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.11 Merging clusters in probabilistic hierarchical clustering: (a) Merging clusters C1 and C2 leads to an increase in overall cluster quality, but merging clusters (b) C3 and (c) C4 does not. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.12 A probabilistic hierarchical clustering algorithm. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.13 Clusters of arbitrary shape. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.14 Density-reachability and density-connectivity in density-based clustering. Source: Based on Ester, Kriegel, Sander, and Xu [EKSX96]. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.15 DBSCAN algorithm. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.16 OPTICS terminology. Source: Based on Ankerst, Breunig, Kriegel, and Sander [ABKS99]. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.17 Cluster ordering in OPTICS. Source: Adapted from Ankerst, Breunig, Kriegel, and Sander [ABKS99]. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.18 The subtlety in density estimation in DBSCAN and OPTICS: Increasing the neighborhood radius slightly from 1 to 2 results in a much higher density. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.19 Hierarchical structure for STING clustering. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.20 Dense units found with respect to age for the dimensions salary and vacation are intersected to provide a candidate search space for dense units of higher dimensionality. Copyright © 2012, Elsevier Inc. All rights Reserved

Copyright © 2012, Elsevier Inc. All rights Reserved 10 Chapter: Data Mining Figure 10.21 A data set that is uniformly distributed in the data space. Copyright © 2012, Elsevier Inc. All rights Reserved