Mining Imperfect Data: Dealing With Contamination And Incomplete Records
by Ronald K. Pearson /
2005 / English / DjVu
3.1 MB Download
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.
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.
