Mining Imperfect Data
Dealing with Contamination and Incomplete Records
(Sprache: Englisch)
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...
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Produktinformationen zu „Mining Imperfect Data “
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.
Inhaltsverzeichnis zu „Mining Imperfect Data “
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.
Autoren-Porträt von Pearson
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.
Bibliographische Angaben
- Autor: Pearson
- 2005, X, 305 Seiten, mit Schwarz-Weiß-Abbildungen, Maße: 17,7 x 25,2 cm, Kartoniert (TB), Englisch
- Verlag: Cambridge University Press
- ISBN-10: 0898715822
- ISBN-13: 9780898715828
Sprache:
Englisch
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