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Handbook of statistical analysis and data mining applications /

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. Th...

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Bibliographic Details
Main Authors: Nisbet, Robert (Author)
Group Author: Miner, Gary; Yale, Ken, D.D.S.; Elder, John F. (John Fletcher); Peterson, Andrew F., 1960-
Published: Academic Press,
Publisher Address: London :
Publication Dates: [2018]
Literature type: Book
Language: English
Edition: Second edition.
Subjects:
Data mining > Statistical methods.
Summary: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas-from science and engineering, to medicine, academia and commerce.
Carrier Form: xxix, 792 pages : color illustrations, portraits (some color), forms ; 24 cm
Bibliography: Includes bibliographical references and index.
ISBN: 9780124166325 (hardback) :
0124166326 (hardback)
Index Number: QA76
CLC: TP311.13
Call Number: TP311.13/N724/2nd ed.
Contents: History of phases of data analysis, basic theory, and the data mining process -- The algorithms and methods in data mining and predictive analytics and some domain areas -- Tutorials and case studies -- Models ensembles, model complexity; using the right model for the right use, significance, ethics, and the future and advanced processes.

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