Mining Imperfect Data: Dealing with Contamination and Incomplete Records. This item is unavailable.Mining Imperfect Data: Dealing with Contamination and Incomplete Records

This book thoroughly discusses the varying problems that occur in data mining, including their sources, consequences, detection, and treatment. Specific strategies for data pretreatment and analytical validation that are broadly applicable are described, making them useful in conjunction with most data mining analysis methods. Examples illustrate the performance of the pretreatment and validation methods in a variety of situations. The book, which deals with a wider range of data anomalies than are usually treated, includes a discussion of detecting anomalies through generalized sensitivity analysis (GSA), a process of identifying inconsistencies using systematic and extensive comparisons of results obtained by analysis of exchangeable datasets or subsets. Real data is made extensive use of, both in the form of a detailed analysis of a few real datasets and various published examples. A succinct introduction to functional equations illustrates their utility in describing various forms of qualitative behavior for useful data characterizations.

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 Universtiy 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.