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Data Mining: Practical Machine Learning Tools and Techniques / Edition 3
     

Data Mining: Practical Machine Learning Tools and Techniques / Edition 3

4.5 2
by Ian H. Witten, Eibe Frank, Mark A. Hall
 

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ISBN-10: 0123748569

ISBN-13: 9780123748560

Pub. Date: 01/20/2011

Publisher: Elsevier Science

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

Overview

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.

Product Details

ISBN-13:
9780123748560
Publisher:
Elsevier Science
Publication date:
01/20/2011
Series:
Morgan Kaufmann Series in Data Management Systems Series
Pages:
664
Sales rank:
216,458
Product dimensions:
7.30(w) x 9.00(h) x 1.50(d)

Table of Contents

Part I: Introduction to data mining

Chapter 1. What’s it all about?

Chapter 2. Input: Concepts, instances, attributes

Chapter 3. Output: Knowledge representation

Chapter 4. Algorithms: The basic methods

Chapter 5. Credibility: Evaluating what’s been learned

Part II: More advanced machine learning schemes

Part II. More advanced machine learning schemes

Chapter 6. Trees and rules

Chapter 7. Extending instance-based and linear models

Chapter 8. Data transformations

Chapter 9. Probabilistic methods

Chapter 10. Deep learning

Chapter 11. Beyond supervised and unsupervised learning

Chapter 12. Ensemble learning

Chapter 13. Moving on: applications and beyond

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Data Mining: Practical Machine Learning Tools and Techniques 4.5 out of 5 based on 0 ratings. 2 reviews.
550Prof More than 1 year ago
Second the review by Predictor. Excellent text, which I use in my graduate-level Data Mining class. Very concise explanations, logical organization, ideal for giving the serious practitioner the reference that is always needed, & the serious student the background necessary to undertake this (still evolving) discipline. What I don't understand is the concept of an electronic version costing more than a physical version of the same tome. Absolute nonsense.
Predictor More than 1 year ago
Context for this review: I am a data miner with 20 years experience, and own the first edition of this book. Good: - Accessible writing style - Broad coverage of algorithms and data mining issues, with an eye toward practical issues - Needless technical trivia (derivations and the like) are avoided - Algorithms are completely spelled out: A competent programmer should be able to turn these descriptions into functioning code. - Third edition makes meaningful improvements on previous editions Bad(ish): - Approximately one-third of this book is now devoted to the WEKA data mining software. I have nothing against WEKA, and it is a good choice for a text such as this, since WEKA is free. In my opinion, though, this coverage consumes too many pages of this book. - Data mining draws from a number of fields with separate roots (statistics, machine learning, pattern recognition, engineering, etc.), and many techniques go by multiple names. As with many other data mining books, this one does not always point out the aliases by which data mining methods are known. The bottom line: This is still the best data mining text on the market.