Data mining : practical machine learning tools and techniques
Author(s)
Bibliographic Information
Data mining : practical machine learning tools and techniques
Elsevier , Morgan Kaufmann, c2011
3rd ed
- : pbk
Available at / 66 libraries
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"Morgan Kaufmann Publishers is an imprint of Elsevier"
"The Morgan Kaufmann series in data management systems"--T.p. verso(CIP)
Includes bibliographical references (p. 587-605) and index
Description and Table of Contents
Description
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.
The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.
Table of Contents
PART I: Introduction to Data MiningCh 1 What's It All About? Ch 2 Input: Concepts, Instances, Attributes Ch 3 Output: Knowledge RepresentationCh 4 Algorithms: The Basic Methods Ch 5 Credibility: Evaluating What's Been Learned PART II: Advanced Data Mining
Ch 6 Implementations: Real Machine Learning SchemesCh 7 Data TransformationCh 8 Ensemble LearningCh 9 Moving On: Applications and BeyondPART III: The Weka Data MiningWorkbenchCh 10 Introduction to WekaCh 11 The ExplorerCh 12 The Knowledge Flow InterfaceCh 13 The ExperimenterCh 14 The Command-Line InterfaceCh 15 Embedded Machine LearningCh 16 Writing New Learning SchemesCh 17 Tutorial Exercises for the Weka Explorer
by "Nielsen BookData"