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Data mining : practical machine learning tools and techniques / Ian. H Witten ... [et al.].

By: Contributor(s): Material type: TextTextPublication details: Amsterdam : Morgan Kaufmann, 2011.Edition: 3rd edDescription: xxxi, 629 p. : ill. ; 23 cmISBN:
  • 9780123748560
Subject(s): DDC classification:
  • 006.312 WIT
Summary: Data Mining: Practical Machine Learning Tools and Techniques 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.
Item type: Books
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Holdings
Current library Call number Copy number Status Barcode
Paro College Library 006.312 WIT (Browse shelf(Opens below)) E19224 Available E19224
Paro College Library 006.312 WIT (Browse shelf(Opens below)) E19225 Available E19225
Paro College Library 006.312 WIT (Browse shelf(Opens below)) E19226 Available E19226

Includes bibliographical references and index.


Data Mining: Practical Machine Learning Tools and Techniques 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.

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