Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Manageme)

by Ian H. Witten, Eibe Frank, and Mark a Hall

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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.
  • ISBN13 9780123748560
  • Publish Date 3 February 2011 (first published 1 January 2011)
  • Publish Status Out of Print
  • Out of Print 4 March 2021
  • Publish Country US
  • Publisher Elsevier Science & Technology
  • Imprint Morgan Kaufmann Publishers In
  • Edition 3rd edition
  • Format Paperback
  • Pages 664
  • Language English