Cover for Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques

A volume in The Morgan Kaufmann Series in Data Management Systems

Book • Third Edition2011

Authors:

Ian H. Witten, Eibe Frank and Mark A. Hall

Data Mining: Practical Machine Learning Tools and Techniques

A volume in The Morgan Kaufmann Series in Data Management Systems

Book • Third Edition2011

 

Cover for Data Mining: Practical Machine Learning Tools and Techniques

Authors:

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 machin ... read full description

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Table of contents

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  2. Book chapterNo access

    References

    Pages 587-605

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    Index

    Pages 607-629

About the book

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.

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.

Key Features

  • Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
  • Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
  • Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
  • Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
  • Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
  • Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Details

ISBN

978-0-12-374856-0

Language

English

Published

2011

Copyright

Copyright © 2011 Elsevier Inc. All rights reserved

Imprint

Morgan Kaufmann

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Authors

Ian H. Witten

Eibe Frank

Mark A. Hall