9780120884070-0120884070-Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

ISBN-13: 9780120884070
ISBN-10: 0120884070
Edition: 2
Author: Ian H. Witten, Eibe Frank
Publication date: 2005
Publisher: Morgan Kaufmann
Format: Paperback 560 pages
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Book details

ISBN-13: 9780120884070
ISBN-10: 0120884070
Edition: 2
Author: Ian H. Witten, Eibe Frank
Publication date: 2005
Publisher: Morgan Kaufmann
Format: Paperback 560 pages

Summary

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) (ISBN-13: 9780120884070 and ISBN-10: 0120884070), written by authors Ian H. Witten, Eibe Frank, was published by Morgan Kaufmann in 2005. With an overall rating of 4.1 stars, it's a notable title among other AI & Machine Learning (Data Mining, Databases & Big Data, Computer Science) books. You can easily purchase or rent Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) (Paperback) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.33.

Description

Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.

The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.

This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.

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