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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 them 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 pre-processing to cluster validation and results interpretation.
This author's unique attention to data recovery methods, theory-based advice, pre- and post-processing issues that are beyond the scope of most texts, 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 · Describes the theory that leads to these methods and relevant interpretation aids, 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 · Offers an up-to-date description of current data mining issues, such as feature selection and cluster validation · Presents a wealth of computational examples covering all stages of clustering
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