Includes bibliographical references and indexes.

Introduction / John A. Hartigan -- An overview of combinatorial data analysis / Phipps Arabie and Lawrence J. Hubert -- Hierarchical classification / Allan D. Gordon -- A hierarchical classes model: Theory and method with applications in psychology and psychopathology / Seymour Rosenberg, Iven Van Mechelen, and Paul De Boeck -- Tree and other network models for representing proximity data / Geert De Soete and J. Douglas Carroll -- Complexity theory: An introduction for practitioners of classification / William H. E. Day -- Neural networks for clustering / Fionn Murtagh -- A review of cluster analysis research in Japan / Akinori Okada -- Clustering and multidimensional scaling in Russia (1960-1990): A review / Boris G. Mirkin and Ilya Muchnik -- Clustering validation: Results and implications for applied analyses / Glenn W. Milligan -- Probability models and hypotheses testing in partitioning cluster analysis / Hans-Hermann Bock.

At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests.

Electronic reproduction. Singapore : World Scientific Publishing Co., 1996. System requirements: Adobe Acrobat Reader. Mode of access: World Wide Web.