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.

work in graph partitioning and in image and market segmentation is related to cluster analysis. 8.1.2 Different Types of Clusterings An entire collection of clusters is commonly referred to as a clustering, and in this section, we distinguish various types of clusterings: hierarchical (nested)

  Analysis, Cluster analysis, Cluster, Segmentation