1. The University of Adelaide, School of Computer Science
9 November 2018
Data Mining: Concepts and Techniques
Chapter 10
Cluster Analysis: Basic Concepts and Methods
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Chapter 2 — Instructions: Language of the Computer

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Figure 10.1 Overview of clustering methods discussed in this chapter. Note that some algorithms may combine various methods.
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Figure 10.2 The k-means partitioning algorithm.
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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 +).
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Figure 10.4 Four cases of the cost function for k-medoids clustering.
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Figure 10.5 PAM, a k-medoids partitioning algorithm.
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Figure 10.6 Agglomerative and divisive hierarchical clustering on data objects {a,b,c,d,e}.
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Figure 10.7 Dendrogram representation for hierarchical clustering of data objects {a,b,c,d,e}.
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Figure 10.8 Hierarchical clustering using single and complete linkages.
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Figure 10.9 CF-tree structure.
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Figure Chameleon: hierarchical clustering based on k-nearest neighbors and dynamic modeling. Source: Based on Karypis, Han, and Kumar [KHK99].
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Figure 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.
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Figure A probabilistic hierarchical clustering algorithm.
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Figure Clusters of arbitrary shape.
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Figure Density-reachability and density-connectivity in density-based clustering. Source: Based on Ester, Kriegel, Sander, and Xu [EKSX96].
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Figure DBSCAN algorithm.
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Figure OPTICS terminology. Source: Based on Ankerst, Breunig, Kriegel, and Sander [ABKS99].
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Figure Cluster ordering in OPTICS. Source: Adapted from Ankerst, Breunig, Kriegel, and Sander [ABKS99].
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Figure The subtlety in density estimation in DBSCAN and OPTICS: Increasing the neighborhood radius slightly from 1 to 2 results in a much higher density.
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Figure Hierarchical structure for STING clustering.
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Figure 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.
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Figure A data set that is uniformly distributed in the data space.
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