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.
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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
Ronald K. Pearson is Senior Scientist with ProSanos Corporation and holds an adjunct faculty position at Jefferson Medical College. His primary research interests are in the areas of nonlinear discrete-time dynamical models, exploratory data analysis, and nonlinear digital signal processing. He is author of the book Discrete-Time Dynamic Models (Oxford University Press, 1999) and coauthor of the book Identification and Control Using Volterra Models (Springer, 2001). He has published three encyclopedia articles and approximately 100 journal and conference papers.
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