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

Witten, Ian H.

ISBN 10: 0120884070 / ISBN 13: 9780120884070

Edité par Morgan Kaufmann, 2005

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Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing and/or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes). N° de réf. du vendeur 000704843U

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Titre : Data Mining: Practical Machine Learning ...

Éditeur : Morgan Kaufmann

Date d'édition : 2005

Reliure : paperback

Etat : Good

Edition : 2nd Edition.

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Synopsis :

Book by Witten Ian H Frank Eibe

Présentation de l'éditeur:

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these 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 for the 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; plus much more.

* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface