book

Data Mining, 3rd Edition

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

February 2011

Beginner to intermediate

664 pages

21h 13m

English

Morgan Kaufmann

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Cover Image
Content
Title
Copyright
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 learning1.2. Simple examples: the weather and other problems1.3. Fielded applications1.4. Machine learning and statistics1.5. Generalization as search1.6. Data mining and ethics1.7. 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 input2.5. Further reading
Chapter 3. Output
3.1. Tables3.2. Linear models3.3. Trees3.4. Rules3.5. Instance-based representation3.6. Clusters3.7. Further Reading
Chapter 4. Algorithms
4.1. InFerring rudimentary rules4.2. Statistical modeling4.3. Divide-and-conquer: constructing decision trees4.4. Covering algorithms: constructing rules4.5. Mining association rules4.6. Linear models4.7. Instance-based learning4.8. Clustering4.9. Multi-instance learning4.10. Further reading4.11. Weka implementations
Chapter 5. Credibility
5.1. Training and testing5.2. Predicting performance5.3. Cross-validation5.4. Other estimates5.5. Comparing data mining schemes5.6. Predicting probabilities5.7. Counting the cost5.8. Evaluating numeric prediction5.9. Minimum description length principle5.10. Applying the MDL principle to clustering5.11. Further reading
PART II. Advanced Data Mining
Chapter 6. Implementations
6.1. Decision trees6.2. Classification rules6.3. Association rules6.4. Extending linear models6.5. Instance-based learning6.6. Numeric prediction with local linear models6.7. Bayesian networks
6.8. Clustering
6.9. Semisupervised learning6.10. Multi-instance learning6.11. Weka implementations
Chapter 7. Data Transformations
7.1. Attribute selection7.2. Discretizing numeric attributes7.3. Projections7.4. Sampling7.5. Cleansing7.6. Transforming multiple classes to binary ones7.7. Calibrating class probabilities7.8. Further reading7.9. Weka implementations
Chapter 8. Ensemble Learning
8.1. Combining multiple models8.2. Bagging8.3. Randomization8.4. Boosting8.5. Additive regression8.6. Interpretable ensembles8.7. Stacking8.8. Further reading8.9. Weka implementations
Chapter 9. Moving on
9.1. Applying data mining9.2. Learning from massive datasets9.3. Data stream learning9.4. Incorporating domain knowledge9.5. Text mining9.6. Web mining9.7. Adversarial situations9.8. Ubiquitous data mining9.9. 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 started11.2. Exploring the explorer11.3. Filtering algorithms11.4. Learning algorithms11.5. Metalearning algorithms11.6. Clustering algorithms11.7. Association-rule learners11.8. Attribute selection
Chapter 12. The Knowledge Flow Interface
12.1. Getting started12.2. Components12.3. Configuring and connecting the components12.4. Incremental learning
Chapter 13. The Experimenter
13.1. Getting started13.2. Simple setup13.3. Advanced setup13.4. The analyze panel13.5. Distributing processing over several machines
Chapter 14. The Command-Line Interface
14.1. Getting started14.2. The structure of weka14.3. 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 classifier16.2. Conventions for implementing classifiers
Chapter 17. Tutorial Exercises for the Weka Explorer
17.1. Introduction to the explorer interface17.2. Nearest-neighbor learning and decision trees17.3. Classification boundaries17.4. Preprocessing and parameter tuning17.5. Document classification17.6. Mining association rules
Index

Content preview from Data Mining, 3rd Edition

PART III

The Weka Data Mining Workbench

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

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Data Mining, 3rd Edition

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

PART III

The Weka Data Mining Workbench

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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