Synopses & Reviews
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.
Synopsis
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.
Synopsis
This book discusses the problems that can occur in data mining, including their sources, consequences, detection and treatment.
Table of Contents
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.