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This text provides an introduction to the practical application of cluster analysis and presents a selection of methods which together can deal with most applications. These methods are chosen for their robustness, consistency and general applicability. The main approaches to clustering are studied and guidance on choosing between the available methods is given. The authors also discuss various types of data, including interval-scaled and binary variables as well as similarity data, and explain ways in which these can be transformed prior to clustering. Numerous exercises are also included.