Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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. - 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
1100665409
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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. - 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
74.95 In Stock
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

by Ian H. Witten, Eibe Frank
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
Add to Wishlist
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

by Ian H. Witten, Eibe Frank

Overview

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. - 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

Product Details

ISBN-13: 9780080477022
Publisher: Morgan Kaufmann Publishers
Publication date: 07/13/2005
Series: The Morgan Kaufmann Series in Data Management Systems
Sold by: Barnes & Noble
Format: eBook
Pages: 560
File size: 11 MB
Note: This product may take a few minutes to download.

About the Author

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.

Table of Contents

Preface1. What's it all about?2. Input: Concepts, instances, attributes3. Output: Knowledge representation4. Algorithms: The basic methods5. Credibility: Evaluating what's been learned6. Implementations: Real machine learning schemes7. Transformations: Engineering the input and output8. Moving on: Extensions and applicationsPart II: The Weka machine learning workbench9. Introduction to Weka10. The Explorer11. The Knowledge Flow interface12. The Experimenter13. The command-line interface14. Embedded machine learning15. Writing new learning schemesReferencesIndex

What People are Saying About This

From the Publisher

Highly anticipated second edition of the highly-acclaimed reference on data mining and machine learning.

From the B&N Reads Blog
Page 1 of

Related Subjects

Computers
Database Management
Data Warehousing & Mining
Computers
Database Management
Data Warehousing & Mining

Customer Reviews