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Book on Data Mining (2002)
Alex A. Freitas
Data Mining and Knowledge Discovery
with Evolutionary Algorithms
Springer-Verlag, 2002
264 pp. ISBN 3-540-43331-7
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- 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
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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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Last modified Thursday August 29 11:16:39 BST 2002
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