Cluster Analysis: Basic Concepts and Algorithms

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8Cluster Analysis: Basic Concepts andAlgorithmsCluster analysis divides data into groups (clusters) that are meaningful, useful,or both. If meaningful groups are the goal, then the clusters should capture thenatural structure of the data. In some cases, however, Cluster analysis is only auseful starting point for other purposes, such as data summarization. Whetherfor understanding or utility, Cluster analysis has long played an importantrole in a wide variety of fields: psychology and other social sciences, biology,statistics, pattern recognition, information retrieval, machine learning, anddata have been many applications of Cluster analysis to practical prob-lems. We provide some specific examples, organized by whether the purposeof the clustering is understanding or for UnderstandingClasses, or conceptually meaningful groupsof objects that share common characteristics, play an important role in howpeople analyze and describe the world.

490 Chapter 8 Cluster Analysis: Basic Concepts and Algorithms broad categories of algorithms and illustrate a variety of concepts: K-means, agglomerative hierarchical clustering, and DBSCAN. The final section of this chapter is devoted to cluster validity—methods for evaluating the goodness of the clusters produced by a clustering algorithm.

  Analysis, Concept, Cluster analysis, Cluster