Handbook of statistical analysis and data mining applications / Robert Nisbet, John Elder, Gary Miner.
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TextLanguage: English Publication details: Amsterdam ; Boston : Academic Press/Elsevier, c2009. Description: 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 LOC classification: QA76.9.D343 | N57 2009Online resources: WorldCat details | Ebook Fulltext | Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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Dr. S. R. Lasker Library, EWU E-book | Non-fiction | 006.312 NIH 2009 (Browse shelf(Opens below)) | Not for loan | ||||
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Dr. S. R. Lasker Library, EWU Circulation Section | Non-fiction | 006.312 NIH 2009 (Browse shelf(Opens below)) | C-2 | Available | 28172 |
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| 006.312 HAD 2012 Data mining : | 006.312 INT 2021 Introduction to data mining / | 006.312 LAT 2022 Text mining for information professionals an uncharted territory / | 006.312 NIH 2009 Handbook of statistical analysis and data mining applications / | 006.312 VAP 2023 Python data science handbook : essential tools for working with data / | 006.32 RAN 2007 Neural networks, fuzzy logic, and genetic algorithms : | 006.32 SII 2006 Introduction to neural networks using MATLAB 6.0 / |
Includes bibliographical references and index.
TOC 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.
AS
Sagar Shahanawaz
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