An introduction to the practical application of cluster analysis, Finding Groups in Data
presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. The text discusses the main approaches to clustering and provides guidance in choosing between the available methods. It also discusses various types of data, including interval-scaled and binary variables as well as similarity data and explains how these can be transformed prior to clustering. With numerous exercises to aid learning, Finding Groups in Data
provides an invaluable introduction to cluster analysis with an emphasis on methods that are both easy to use and modern.