Data Mining: Practical Machine Learning Tools and Techniques

Frank, E. and Witten, I.H.

ISBN 10: 0120884070 / ISBN 13: 9780120884070

Edité par Morgan Kaufmann, 2005

A propos de cet article

This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1250grams, ISBN:9780120884070.

N° de réf. du vendeur 9830346

Détails bibliographiques

• Titre: Data Mining: Practical Machine Learning Tools and Techniques
• Éditeur: Morgan Kaufmann
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 0120884070
• ISBN à 13 chiffres: 9780120884070
• Reliure: Couverture souple
• Édition: 2ème Édition
• État de l'article: Good
• Catalogues du vendeur:
  • Computing

A propos de ce titre

Synopsis

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.

À propos des auteurs

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.

Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now a professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.