Data Mining,

Edition 3 Practical Machine Learning Tools and Techniques

Editors: By Ian H. Witten, Eibe Frank and Mark A. Hall

Publication Date: 06 Jan 2011

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Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 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.

The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

Key Features

Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects
Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

About the author

By Ian H. Witten, Computer Science Department, University of Waikato, New Zealand; Eibe Frank, Computer Science Department, University of Waikato, New Zealand. and Mark A. Hall, Computer Science Department, University of Waikato, New Zealand.

LIST OF FIGURES
LIST OF TABLES
PREFACE
ACKNOWLEDGMENTS
ABOUT THE AUTHORS
PART I. Introduction to Data Mining

CHAPTER 1. What’s It All About?

1.1. Data mining and machine learning
1.2. Simple examples: the weather and other problems
1.3. Fielded applications
1.4. Machine learning and statistics
1.5. Generalization as search
1.6. Data mining and ethics
1.7. Further reading

CHAPTER 2. Input

2.1. What’s a concept?
2.2. What’s in an example?
2.3. What’s in an attribute?
2.4. Preparing the input
2.5. Further reading

CHAPTER 3. Output

3.1. Tables
3.2. Linear models
3.3. Trees
3.4. Rules
3.5. Instance-based representation
3.6. Clusters
3.7. Further Reading

CHAPTER 4. Algorithms

4.1. InFerring rudimentary rules
4.2. Statistical modeling
4.3. Divide-and-conquer: constructing decision trees
4.4. Covering algorithms: constructing rules
4.5. Mining association rules
4.6. Linear models
4.7. Instance-based learning
4.8. Clustering
4.9. Multi-instance learning
4.10. Further reading
4.11. Weka implementations

CHAPTER 5. Credibility

5.1. Training and testing
5.2. Predicting performance
5.3. Cross-validation
5.4. Other estimates
5.5. Comparing data mining schemes
5.6. Predicting probabilities
5.7. Counting the cost
5.8. Evaluating numeric prediction
5.9. Minimum description length principle
5.10. Applying the MDL principle to clustering
5.11. Further reading

PART II. Advanced Data Mining

CHAPTER 6. Implementations

6.1. Decision trees
6.2. Classification rules
6.3. Association rules
6.4. Extending linear models
6.5. Instance-based learning
6.6. Numeric prediction with local linear models
6.7. Bayesian networks
6.8. Clustering
6.9. Semisupervised learning
6.10. Multi-instance learning
6.11. Weka implementations

CHAPTER 7. Data Transformations

7.1. Attribute selection
7.2. Discretizing numeric attributes
7.3. Projections
7.4. Sampling
7.5. Cleansing
7.6. Transforming multiple classes to binary ones
7.7. Calibrating class probabilities
7.8. Further reading
7.9. Weka implementations

CHAPTER 8. Ensemble Learning

8.1. Combining multiple models
8.2. Bagging
8.3. Randomization
8.4. Boosting
8.5. Additive regression
8.6. Interpretable ensembles
8.7. Stacking
8.8. Further reading
8.9. Weka implementations

CHAPTER 9. Moving on

9.1. Applying data mining
9.2. Learning from massive datasets
9.3. Data stream learning
9.4. Incorporating domain knowledge
9.5. Text mining
9.6. Web mining
9.7. Adversarial situations
9.8. Ubiquitous data mining
9.9. Further reading

PART III. The Weka Data Mining Workbench

CHAPTER 10. Introduction to Weka

10.1. What’s in weka?
10.2. How do you use it?
10.3. What else can you do?
10.4. How do you get it?

CHAPTER 11. The Explorer

11.1. Getting started
11.2. Exploring the explorer
11.3. Filtering algorithms
11.4. Learning algorithms
11.5. Metalearning algorithms
11.6. Clustering algorithms
11.7. Association-rule learners
11.8. Attribute selection

CHAPTER 12. The Knowledge Flow Interface

12.1. Getting started
12.2. Components
12.3. Configuring and connecting the components
12.4. Incremental learning

CHAPTER 13. The Experimenter

13.1. Getting started
13.2. Simple setup
13.3. Advanced setup
13.4. The analyze panel
13.5. Distributing processing over several machines

CHAPTER 14. The Command-Line Interface

14.1. Getting started
14.2. The structure of weka
14.3. Command-line options

CHAPTER 15. Embedded Machine Learning

15.1. A simple data mining application

CHAPTER 16. Writing New Learning Schemes

16.1. An example classifier
16.2. Conventions for implementing classifiers

CHAPTER 17. Tutorial Exercises for the Weka Explorer

17.1. Introduction to the explorer interface
17.2. Nearest-neighbor learning and decision trees
17.3. Classification boundaries
17.4. Preprocessing and parameter tuning
17.5. Document classification
17.6. Mining association rules

Index

ISBN: 9780123748560

Page Count: 664

Retail Price (USD) :

*Witten & Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2e (MK 2005). 560 pp, $62.95/51.95 paperback (9780120884070) Bookscan: 8,881*Han & Kamber, Data Mining: Concepts and Techniques, 2e (MK 2006). 800 pp, $64.95 hardback (9781558609013) Bookscan: 2,811

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Information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals, as well as professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

Data Mining,

Editors: By Ian H. Witten, Eibe Frank and Mark A. Hall

Description

Key Features

About the author

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