Clustering and classification
著者
書誌事項
Clustering and classification
World Scientific, c1996
- : hbk
- : pbk
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注記
Includes references and indexes
内容説明・目次
- 巻冊次
-
: hbk ISBN 9789810212872
内容説明
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.
目次
- An overview of combinatorial data analysis, P.Arabia and L.Hubert
- hierarchical classification, A.D.Gordon
- comparison of hierarchical clustering algorithms based on space-distorting properties, N.Ohsumi and N.Nakamura
- a hierchical classes model
- theory and method with applications in psychology and psychopathology, S.Rosenberg et al
- an overview of additive trees and other network models, J.D.Carroll and G.De Soete
- clusterwise aggregation of relations, W.Gaul and M.Schader
- complexity theory: an introduction for practitioners of classification, W.H.E.Day
- neural networks for clustering, F.Murtagh
- variable selection and weighting in cluster analysis, G.De Soete
- an overview of latent class approaches to cluster analysis, W.S.DeSarbo and G.de Soete
- a review of cluster analysis research in Japan, A.Okada
- clustering and multidimensional scaling in Russia (1960-1990): a review, B.G.Mirkin and I.Muchnik
- practical advances in cluster analysis, G.W.Milligan
- significance tests in cluster analysis, H.H.Bock.
- 巻冊次
-
: pbk ISBN 9789810213541
内容説明
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.
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