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Handbook of statistical analysis and data mining applications / Robert Nisbet, John Elder, Gary Miner.

By: Nisbet, Robert.
Contributor(s): Elder, John F | Miner, Gary.
Material type: TextTextPublisher: Amsterdam ; Boston : Academic Press/Elsevier, c2009Description: xxxiv, 824 p. : ill. (chiefly col.) ; 25 cm. +.ISBN: 9780123747655 (hbk. : alk. paper); 0123747651 (hbk. : alk. paper); 9780123750860 (DVDROM); 0123750865 (DVDROM).Other title: Handbook of statistical analysis & data mining applications [Cover title].Subject(s): Data mining -- Statistical methodsDDC classification: 006.312 Online resources: WorldCat details | Ebook Fulltext
Contents:
Table of contents History of phases of data analysis, basic theory, and the data mining process -- The algorithms in data mining and text mining, the organization of the three most common data mining tools, and selected specialized areas using data mining -- Tutorials--step-by-step case studies as a starting point to learn how to do data mining analyses -- Measuring true complexity, the "right model for the right use," top mistakes, and the future of analytics.
Summary: Guides business analysts, scientists, engineers and researchers through all stages of data analysis, model building and implementation. This book helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application.
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Non-fiction 006.312 NIH 2009 (Browse shelf) Not for loan
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Includes bibliographical references and index.

Table of contents History of phases of data analysis, basic theory, and the data mining process -- The algorithms in data mining and text mining, the organization of the three most common data mining tools, and selected specialized areas using data mining -- Tutorials--step-by-step case studies as a starting point to learn how to do data mining analyses -- Measuring true complexity, the "right model for the right use," top mistakes, and the future of analytics.

Guides business analysts, scientists, engineers and researchers through all stages of data analysis, model building and implementation. This book helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application.

Applied Statistics

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