Rather than a traditional set of ad hoc techniques, Mirkin (computer science, University of London, UK) presents a theory that not only closes the gaps in K-Means and Ward methods for clustering data, but also extends the methods into areas of current interest, such as clustering mixed scale data and incomplete clustering. The author suggests original methods for both cluster finding and cluster description, addresses related topics such as principal component analysis, and provides nearly 60 examples covering all stages of clustering. The book can be used for teaching, for self-study, and as a professional reference. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)