
Mining Imperfect Data
Dealing with Contamination and Incomplete Records
Ronald K. Pearson(Author)
Society for Industrial and Applied Mathematics (SIAM) (Publisher)
Published on 1. April 2005
Book
Paperback/Softback
184 pages
978-0-89871-582-8 (ISBN)
Description
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.
More details
Language
English
Place of publication
Philadelphia
United States
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 252 mm
Width: 177 mm
Thickness: 19 mm
Weight
552 gr
ISBN-13
978-0-89871-582-8 (9780898715828)
Schweitzer Classification
Content
Preface; 1. Introduction; 2. Imperfect datasets; 3. Univariate outlier detection; 4. Data pretreatment; 5. What is a 'good' data characterization?; 6. Generalized sensitivity analysis; 7. Sampling schemes for a fixed dataset; 8. Concluding remarks and open questions; Bibliography; Index.