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Clustering And Classification
English
463 b
30-day return policy
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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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