- Paperback: 305 pages
- Publisher: SIAM: Society for Industrial and Applied Mathematics (April 4, 2005)
- Language: English
- ISBN-10: 0898715822
- ISBN-13: 978-0898715828
- Product Dimensions: 6 x 0.6 x 9 inches
- Shipping Weight: 1.2 pounds
- Average Customer Review: Be the first to review this item
- Amazon Best Sellers Rank: #3,716,659 in Books (See Top 100 in Books)
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Mining Imperfect Data: Dealing with Contamination and Incomplete Records
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'An accessible presentation of statistical methods and analysis to deal with imperfect data in real data mining applications.' Joydeep Ghosh, University of Texas at Austin
'An appealing feature of this book is the use of fresh datasets that are much larger than those currently found in standard books on outliers and statistical diagnostics.' Anthony Atkinson, London School of Economics
'The book provides the reader with clear descriptions and accessible discussions of problems, motivations, methods and interpretations. The author excels when describing the importance of tools and their applications. The first chapter is a prime example of how to introduce important concepts and methods to a wide readership that may be composed of students, researchers and non-specialists.' Francisco J. Azuaje, BioMedical Engineering OnLine
Data mining is concerned with the analysis of databases large enough that various anomalies, including outliers, incomplete data records, and more subtle phenomena such as misalignment errors, are virtually certain to be present. This book describes in detail a number of these problems including their sources, consequences, detection and treatment.