Data Mining: Practical Machine Learning Tools and Techniques, 2/e

Ian H. Witten, Eibe Frank




As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these 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 for the 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; plus much more.

Table of Contents


1. What?s it all about?
1.1 Data mining and machine learning 1.2 Simple examples: the weather problem and others 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

2. Input: Concepts, instances, attributes
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

3. Output: Knowledge representation
3.1 Decision tables 3.2 Decision trees 3.3 Classification rules 3.4 Association rules 3.5 Rules with exceptions 3.6 Rules involving relations 3.7 Trees for numeric prediction 3.8 Instance-based representation 3.9 Clusters 3.10 Further reading

4. Algorithms: The basic methods
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 Further reading

5. Credibility: Evaluating what?s been learned
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 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading

6. Implementations: Real machine learning schemes
6.1 Decision trees 6.2 Classification rules 6.3 Extending linear models 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering 6.7 Bayesian networks

7. Transformations: Engineering the input and output
7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Some useful transformations 7.4 Automatic data cleansing 7.5 Combining multiple models 7.6 Using unlabeled data 7.7 Further reading

8. Moving on: Extensions and applications
8.1 Learning from massive datasets 8.2 Incorporating domain knowledge 8.3 Text and Web mining 8.4 Adversarial situations 8.5 Ubiquitous data mining 8.6 Further reading

Part II: The Weka machine learning workbench

9. Introduction to Weka
9.1 What?s in Weka? 9.2 How do you use it? 9.3 What else can you do?

10. The Explorer
10.1 Getting started 10.2 Exploring the Explorer 10.3 Filtering algorithms 10.4 Learning algorithms 10.5 Meta-learning algorithms 10.6 Clustering algorithms 10.7 Association-rule learners 10.8 Attribute selection

11. The Knowledge Flow interface
11.1 Getting started 11.2 Knowledge Flow components 11.3 Configuring and connecting the components 11.4 Incremental learning

12. The Experimenter
12.1 Getting started 12.2 Simple setup 12.3 Advanced setup 12.4 The Analyze panel 12.5 Distributing processing over several machines

13. The command-line interface
13.1 Getting started 13.2 The structure of Weka 13.3 Command-line options

14. Embedded machine learning

15. Writing new learning schemes
References Index