Covering both classical and modern approaches including K-Means and divisive clustering, this book uses in-depth case studies to illustrate how clustering methods can be applied. The case studies have been expanded and improved in this second edition. The author also presents new material on variable selection and weighting, similarity/relational data clustering, spectral clustering, and interpretation of clusters. This edition is supplemented with a website that includes MATLAB code and datasets for all of the examples presented in the text.