Data mining : practical machine learning tools and techniques

Title:

ISBN:

9780123748560

Personal Author:

Witten, I. H. (Ian H.)

Call Number:

QA 76.9 .D343 W58 2011

Edition:

3rd ed.

Publication Information:

Burlington, MA : Morgan Kaufmann, c2011.

Physical Description:

xxxiii, 629 p. : ill. ; 24 cm.

Series:

[Morgan Kaufmann series in data management systems]

Morgan Kaufmann series in data management systems.

Contents:

Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.

Subject Term:

Data mining.

Subsequent Author values, for Searching:

Frank, Eibe.

Hall, Mark A.

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Material Type	Item Barcode	Call Number	Location
Book	IPB292001	QA 76.9 .D343 W58 2011	Searching... Unknown

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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.

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 an associate 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.>

Mark A. Hall holds a bachelor's degree in computing and mathematical sciences and a Ph.D. in computer science, both from the University of Waikato. Throughout his time at Waikato, as a student and lecturer in computer science and more recently as a software developer and data mining consultant for Pentaho, an open-source business intelligence software company, Mark has been a core contributor to the Weka software described in this book. He has published a number of articles on machine learning and data mining and has refereed for conferences and journals in these areas.

Part I Machine Learning Tools and Techniques

Ch 1 What's It All About?

Ch 2 Input: Concepts, Instances, Attributes

Ch 3 Output: Knowledge Representation: Algorithms

Ch 4 The Basic Methods

Ch 5 Credibility: Evaluating What's Been Learned

Ch 6 Implementations: Real Machine Learning Schemes

Ch 7 Data Transformation

Ch 8 Ensemble Learning

Ch 9 Massive Data Sets

Ch 10 Practical Data Mining

Part II The Weka Machine Learning Workbench

Ch 11 Intro to Weka

Ch 12 The Explorer

Ch 13 The Knowledge Flow Interface

Ch 14 The Experimenter

Ch 15 The Command-Line Interface

Ch 16 Embedded Machine Learning

Ch 17 Writing New Learning Schemes

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Table of Contents