Revue de presse
"The authors provide enough theory to enable practical application, and it is this practical focus that separates this book from most, if not all, other books on this subject."- Dorian Pyle, Director of Modeling at Numetrics and an internationally known author of Data Preparation for Data Mining (Morgan Kaufmann, 1999) and Business Modeling for Data Mining (Morgan Kaufmann, 2003)
"This book would be a strong contender for a technical data mining course. It is one of the best of its kind."- Herb Edelstein, Principal, Data Mining Consultant, Two Crows Consulting.
"It is certainly one of my favorite data mining books in my library"- Tom Breur, Principal, XLNT Consulting, Tilburg, The Netherlands --Tom Breur, Principal, XLNT Consulting, Tilburg, The Netherlands
Présentation de l'éditeur
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.
Biographie de l'auteur
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.
