Clustering for Data Mining: A Data Recovery Approach (Chapman and Hall/CRC Computer Science and Data Analysis)

Mirkin, Boris

ISBN 10: 1584885343 / ISBN 13: 9781584885344

Published by Chapman and Hall/CRC, 2005

About this Item

VG condition book without dust jacket. Boards are clean with little wear. Book has clean and bright contents.

Seller Inventory # 9999-9991013162

Bibliographic Details

• Title: Clustering for Data Mining: A Data Recovery Approach (Chapman and Hall/CRC Computer Science and Data Analysis)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2005
• Language: English
• ISBN 10: 1584885343
• ISBN 13: 9781584885344
• Binding: Hardcover
• Condition: Good

About this title

Synopsis

Often considered more as an art than a science, the field of clustering has been dominated by learning through examples and by techniques chosen 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 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 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.

About the Author

Boris Mirkin is a professor of computer science at the University of London, UK.