Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organising multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques are applicable in a wide range of areas such as medicine, psychology and market research. This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.
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This book is an in depth presentation of clustering. Concepts are explained well. There aren't many books devoted entirely to cluster analysis, but this is the best of those I have seen.
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applications approach average linkage Banfield and Raftery binary categorical centroid Chapter Clustan procedure cluster analysis cluster criteria cluster solution cluster structure clustering algorithm clustering methods clustering techniques CM CM CM coefficient command cluster Computer constraints covariance matrices criterion data matrix data set defined dendrogram described det(W dissimilarity matrix dissimilarity measures distance matrix distance measures distribution estimated Euclidean distance Everitt example fc-means Figure Genstat Gower and Legendre Hartigan hierarchical clustering hierarchical methods individuals Journal of Classification Kaufman and Rousseeuw latent class analysis maximum likelihood membership minimization Minitab mixture densities mixture model neural network nodes number of clusters number of groups objects observations optimization overlapping clusters package parameters partitions points projection pursuit proximity matrix proximity measures Rand index Rousseeuw 1990 S-PLUS sample SAS Institute Section self-organizing map silhouette plot similarity similarity matrix single linkage SPSS standard Statistical suggested SYSTAT Table trace(W tree values variables weights Wishart within-group