This volume incorporates many methodologies developed since the publication of the first edition. Like its predecessor, it describes both the clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.