Handbook of cluster analysis / [edited by] Christian Hennig...[et al.].

Includes bibliographical references and index.

Section I. Optimization methods --
Section II. Dissimilarity-based methods --
Section III. Methods based on probability models --
Section IV. Methods based on density modes and level sets --
Section V. Specific cluster and data formats --
Section VI. Cluster validation and further general issues.

The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster.