Data Mining
Practical Machine Learning Tools and Techniques, Second Edition
2nd Edition
Published on 13. July 2005
Book
Paperback/Softback
560 pages
978-0-12-088407-0 (ISBN)
Description
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.
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.
Reviews / Votes
"This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate. If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start.? --From the foreword by Jim Gray, Microsoft Research"It covers cutting-edge, data mining technology that forward-looking organizations use to successfully tackle problems that are complex, highly dimensional, chaotic, non-stationary (changing over time), or plagued by. The writing style is well-rounded and engaging without subjectivity, hyperbole, or ambiguity. I consider this book a classic already!? --Dr. Tilmann Bruckhaus, StickyMinds.com
More details
Series
Edition
2nd edition
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
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
Edition type
New edition
Dimensions
Height: 235 mm
Width: 191 mm
Weight
1116 gr
ISBN-13
978-0-12-088407-0 (9780120884070)
Schweitzer Classification
Persons
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.
Content
Preface
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
References
Index
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
References
Index