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Practical Machine Learning Tools and Techniques, Second Edition
To order this title, and for more information, click here
Second Edition
By
Ian Witten, University of Waikato, Hamilton, New Zealand.
Eibe Frank, University of Waikato, Hamilton, New Zealand. Recipient of the 2005 ACM SIGKDD Service Award.
Description
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.
Audience
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
Contents
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
| Bibliographic details |
Paperback, 560 pages, publication date: JUN-2005
ISBN-13: 978-0-12-088407-0
ISBN-10: 0-12-088407-0
Imprint: MORGAN KAUFFMAN
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USD 65.95
GBP 38.99
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Last update: 5 Sep 2009
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