edited by P Arabie (Rutgers Univ.), L J Hubert (Univ. Illinois), & G De Soete (Univ. Ghent)

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.

• An Overview of Combinatorial Data Analysis (P Arabie & L J Hubert)
• Hierarchical Classification (A D Gordon)
• A Hierarchical Classes Model: Theory and Method with Applications in Psychology and Psychopathology (S Rosenberg et al.)
• Trees and Other Network Models for Representing Proximity Data (G De Soete & J D Carroll)
• Complexity Theory: An Introduction for Practitioners of Classification (W H E Day)
• Neural Networks for Clustering (F Murtagh)
• A Review of Cluster Analysis Research in Japan (A Okada)
• Clustering and Multidimensional Scaling in Russia (1960–1990): A Review (B G Mirkin & I Muchnik)
• Clustering Validation: Results and Implications for Applied Analyses (G W Milligan)
• Probability Models and Hypotheses Testing in Partitioning Cluster Analysis (H-H Bock)