Data Mining

Practical Machine Learning Tools and Techniques

Ian H. Witten, Eibe Frank

550 pages, parution le 02/08/2005 (2^eme édition)

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Résumé

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.

L'auteur - Ian H. Witten

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.

Sommaire

Part I : Machine Learning Tools and Techniques
- 1. What's it all about?
- 2. Input: Concepts, instances, attributes
- 3. Output: Knowledge representation
- 4. Algorithms: The basic methods
- 5. Credibility: Evaluating what's been learned
- 6. Implementations: Real machine learning schemes
- 7. Transformations: Engineering the input and output
- 8. Moving on: Extensions and applications
Part II: The Weka machine learning workbench
- 9. Introduction to Weka
- 10. The Explorer
- 11. The Knowledge Flow interface
- 12. The Experimenter
- 13. The command-line interface
- 14. Embedded machine learning
- 15. Writing new learning schemes

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Morgan Kaufmann
Auteur(s)	Ian H. Witten, Eibe Frank
Parution	02/08/2005
Édition	2^eme édition
Nb. de pages	550
Format	19 x 23
Couverture	Broché
Poids	1130g
Intérieur	Noir et Blanc
EAN13	9780120884070
ISBN13	978-0-12-088407-0