Suited to the advanced undergraduate and postgraduate student of classification, cluster analysis, and multivariate analysis, as well as researchers in many disciplines, this volume addresses clustering and graphical methods of representing data, as well as providing advice on ways to decide on the relevant methods of analysis for different data sets. First published in 1981, the new edition contains a substantial amount of new material including an overview of recent methodological developments in efficient clustering algorithms, cluster validation, consensus classifications, and classification of symbolic data.