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New Book on Data Mining


Alex A. Freitas
Data Mining and Knowledge Discovery
with Evolutionary Algorithms
Springer-Verlag, 2002
264 pp. ISBN 3-540-43331-7


TABLE OF CONTENTS

1 INTRODUCTION
1.1 Data Mining and Knowledge Discovery
1.2 Knowledge Representation
1.3 An Overview of Data Mining Paradigms
References

2 DATA MINING TASKS AND CONCEPTS
2.1 Classification
2.2 Dependence Modeling
2.3 The Challenge of Measuring Prediction-Rule Quality
2.4 Clustering
2.5 Inductive Bias
References

3 DATA MINING PARADIGMS
3.1 Decision-Tree Building Algorithms
3.2 Rule Induction Algorithms
3.3 Instance-Based Learning (Nearest Neighbor) Algorithms
References

4 DATA PREPARATION
4.1 Attribute Selection
4.2 Discretization of Continuous Attributes
4.3 Attribute Construction
References

5 BASIC CONCEPTS OF EVOLUTIONARY ALGORITHMS
5.1 An Overview of Evolutionary Algorithms (EAs)
5.2 Selection Methods
5.3 Genetic Algorithms (GA)
5.4 Genetic Programming
5.5 Niching
References

6 GENETIC ALGORITHMS FOR RULE DISCOVERY
6.1 Individual Representation
6.2 Task-Specific Generalizing/Specializing Operators
6.3 Task-Specific Population Initialization and Seeding
6.4 Task-Specific Rule-Selection Methods
6.5 Fitness Evaluation
References

7 GENETIC PROGRAMMING FOR RULE DISCOVERY
7.1 The Problem of Closure in GP for Rule Discovery
7.2 Booleanizing All Terminals
7.3 Constrained-Syntax and Strongly-Typed GP
7.4 Grammar-Based GP for Rule Discovery
7.5 GP for Decision-Tree Building
7.6 On the Quality of Rules Discovered by GP
References

8 EVOLUTIONARY ALGORITHMS FOR CLUSTERING
8.1 Cluster Description-Based Individual Representation
8.2 Centroid/Medoid-Based Individual Representation
8.3 Instance-Based Individual Representation
8.4 Fitness Evaluation
8.5 EAs vs Conventional Clustering Techniques
References

9 EVOLUTIONARY ALGORITHMS FOR DATA PREPARATION
9.1 EAs for Attribute Selection
9.2 EAs for Attribute Weighting
9.3 Combining Attribute Selection and Attribute Weighting
9.4 GP for Attribute Construction
9.5 Combining Attribute Selection and Construction with a Hybrid GA/GP
References

10 EVOLUTIONARY ALGORITHMS FOR DISCOVERING FUZZY RULES
10.1 Basic Concepts of Fuzzy Sets
10.2 Fuzzy Prediction Rules vs Crisp Prediction Rules
10.3 A Simple Taxonomy of EAs for Fuzzy-Rule Discovery
10.4 Using EAs for Generating Fuzzy Rules
10.5 Using EAs for Tuning Membership Functions
10.6 Using EAs for Both Generating Fuzzy Rules and Tuning Membership Functions
10.7 Fuzzy Fitness Evaluation
References

11 SCALING UP EVOLUTIONARY ALGORITHMS FOR LARGE DATA SETS
11.1 Using Data Subsets in Fitness Evaluation
11.2 An Overview of Parallel Processing
11.3 Parallel EAs for Data Mining
References

12 CONCLUSIONS AND RESEARCH DIRECTIONS
12.1 General Remarks on Data Mining with EAs
12.2 Research Directions
References

INDEX

Publisher's Address for Ordering the Book:

Springer
Customer Service
Haberstr. 7
69126 Heidelberg
Germany
Fax: ++49 (0)6221 345 229
E-mail: orders@springer.de



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