Data Mining
Practical Machine Learning Tools and Techniques, Second Edition
- 2nd Edition - July 13, 2005
- Latest edition
- Authors: Ian H. Witten, Eibe Frank
- Language: English
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic cor… Read more
Data Mining, Second Edition, describes data mining 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 of this 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; and much more.
This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
- Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods
- Performance improvement techniques that work by transforming the input or output
Information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses
Preface
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
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
- Edition: 2
- Latest edition
- Published: July 13, 2005
- Language: English
IW
Ian H. Witten
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.
Affiliations and expertise
Computer Science Department, University of Waikato, New ZealandEF
Eibe Frank
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 a 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.
Affiliations and expertise
Computer Science Department, University of Waikato, New Zealand