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Data Mining : Practical Machine Learning Tools and Techniques, Second Edition.

Author: I H Witten; Eibe Frank; ebrary, Inc.
Publisher: San Diego : Elsevier Science & Technology Books ; Los Angeles : Sony Electronics [distributor]
Series: Data Mining, the Morgan Kaufmann Ser. in Data Management Systems Ser.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Witten, Ian H.
Data Mining : Practical Machine Learning Tools and Techniques, Second Edition
Burlington : Elsevier Science,c2014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: I H Witten; Eibe Frank; ebrary, Inc.
ISBN: 9780080477022 008047702X 9780120884070 0120884070
OCLC Number: 842281578
Description: 1 online resource.
Contents: Foreword; Preface; PART I: MACHINE LEARNING TOOLS AND TECHNIQUES; 1 What's it all about?; 2 Input: Concepts, instances, and attributes; 3 Output: Knowledge representation; 4 Algorithms: The basic methods; 5 Credibility: Evaluating what's been learned; 6 Implementations: Real machine learning schemes; 7 Transformations: Engineering the input and output; 8 Moving on: Extensions and applications; PART II: THE WEKA MACHINE LEARNING WORKBENCH; 9 Introduction to Weka; 10 The Explorer; 11 The Knowledge Flow Interface; 12 The Experimenter; 13 The Command-Line Interface 14 Embedded machine learning15 Writing New Learning Schemes; References; Index; About the Authors
Series Title: Data Mining, the Morgan Kaufmann Ser. in Data Management Systems Ser.
Other Titles: Ebrary Academic Complete Subscription Collection.

Abstract:

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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

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