Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by choosing techniques almost through trial-and-error. Even the most popular clustering methods - K-Means for partitioning the data set and Ward's method for hierarchical clustering - have lacked the theoretical attention that would establish a firm relationship between the two methods and provide relevant interpretation aids. Rather than the traditional set of ad hoc techniques, Clustering for Data Mining: A Data Recovery Approach presents a theory that not only closes gaps in K-Means and Ward methods, 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, contingency measures, and data visualization; and includes nearly 60 computational examples covering all stages of clustering, from data preprocessing to cluster validation and results interpretation. This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues and clear, practical instructions for real-world data mining make this book ideally suited for virtually all purposes: for teaching, for self-study, and for professional reference. Features Introduces classical clustering methods extended, via the data recovery approach, to modern data mining tasks Fills gaps in the established theory and corrects common misconceptions Treats the two most popular methods, K-Means and Ward clustering, offering the first theoretically motivated instructions for automating all steps of data mining with clustering Presents a wealth of computational examples covering all stages of clustering