Classification, Clustering, and Data Analysis Recent Advances and Applications [Paperback]

The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

The present volume contains a selection of papers presented at the Eighth Conference of the International Federation of Classification Societies (IFCS) which was held in Cracow, Poland, July 16-19, 2002. All originally submitted papers were subject to a reviewing process by two independent referees, a procedure which resulted in the selection of the 53 articles presented in this volume. These articles relate to theoretical investigations as well as to practical applications and cover a wide range of topics in the broad domain of classifi? cation, data analysis and related methods. If we try to classify the wealth of problems, methods and approaches into some representative (partially over? lapping) groups, we find in particular the following areas: Clustering Cluster validation Discrimination Multivariate data analysis Statistical methods Symbolic data analysis Consensus trees and phylogeny Regression trees Neural networks and genetic algorithms Applications in economics, medicine, biology, and psychology. Given the international orientation of IFCS conferences and the leading role of IFCS in the scientific world of classification, clustering and data anal? ysis, this volume collects a representative selection of current research and modern applications in this field and serves as an up-to-date information source for statisticians, data analysts, data mining specialists and computer scientists.I. Clustering and Discrimination.- Clustering.- Some Thoughts about Classification.- Partial Defuzzification of Fuzzy Clusters.- A New Clustering Approach, Based on the Estimation of the Probability Density Function, for GlÈ