Data mining : practical machine learning tools and techniques Cover Image

E-book

Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.

Witten, I. H. (Ian H.), (author.). Frank, Eibe, (author.). Hall, Mark A. (Mark Andrew), (author.).

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.

Record details

ISBN: 9780123748560
ISBN: 0123748569
ISBN: 9780080890364
ISBN: 0080890369
Physical Description: 1 online resource (xxxiii, 629 pages) : illustrations
Edition: 3rd ed.
Publisher: Burlington, MA : Morgan Kaufmann, ©2011.

Content descriptions

Bibliography, etc. Note:	Includes bibliographical references and index.
Formatted Contents Note:	What's it all about? -- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Data transformation -- Ensemble learning -- Moving on : applications and beyond -- Introduction to Weka -- The explorer -- The knowledge flow interface -- The experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes -- Tutorial exercises for the weka explorer.
Source of Description Note:	Print version record.