Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration


  • Earl Cox, Scianta Intelligence, LLC, Chapel Hill, NC

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As you’ll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems.You don’t need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.
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Researchers and technicians in organizations with large databases.


Book information

  • Published: January 2005
  • ISBN: 978-0-12-194275-5

Table of Contents

PrefaceAcknowledgementsIntroductionPART ONE – CONCEPTS AND ISSUESChapter 1. FOUNDATIONS AND IDEAS1.1 Enterprise Applications and Analysis Models1.2 Distributed and Centralized Repositories1.3 The Age of Distributed Knowledge1.4 Information and Knowledge Discovery1.5 Data Mining and Business Models1.6 Fuzzy Systems for Business Process Models1.7 Evolving Distributed Fuzzy Models1.8 A Sample Case – Evolving a Model for Customer SegmentationReviewChapter 2. PRINCIPAL MODEL TYPES2.1 Model and Event State Categorization2.2 Model Type and Outcome CategorizationReviewChapter 3. APPROACHES TO MODEL BUILDING3.1 Ordinary Statistics.3.2 Non-Parametric Statistics3.3 Linear Regression In Statistical Models3.4 Non-Linear Growth Curve Fitting3.5 Cluster Analysis3.6 Decision Trees and Classifiers3.7 Neural Networks3.8 Fuzzy SQL Systems3.9 Rule Induction and Dynamic Fuzzy ModelsReviewReferencesPART TWO – FUZZY SYSTEMSChapter 4. FUNDAMENTAL CONCEPTS OF FUZZY LOGIC4.1 The Vocabulary of Fuzzy Logic4.2 Boolean (Crisp) Sets – The Law of Bivalence4.3 Fuzzy SetsReviewChapter 5. FUNDAMENTAL CONCEPTS OF FUZZY SYSTEMS5.1 The Vocabulary of Fuzzy Systems5.2 Fuzzy Rule-Based Systems – An Overview5.3 Fuzzy Rules5.4 Variable Decomposition Into Fuzzy Sets5.5 A Fuzzy Knowledge Base – The Details5.6 The Fuzzy Inference Engine5.7 Inference Engine Approaches5.8 Running A Fuzzy ModelReviewChapter 6. FUZZYSQL AND INTELLIGENT QUERIES6.1 The Vocabulary of Relational Databases and Queries6.2 Basic Relational Database Concepts6.3 Structured Query Language Fundamentals6.4 Precision and Accuracy6.5 Why do we search a database?6.6 Expanding the Query Scope6.7 Fuzzy Query Fundamentals6.8 Measuring Query Compatibility6.9 Complex Query Compatibility Metrics6.10 Compatibility Threshold Management6.11 FuzzySQL Process Flow6.12 FuzzySQL Example6.13 Evaluating the FuzzySQL OutcomesReviewReferencesChapter 7. FUZZY CLUSTERING7.1 The Vocabulary of Fuzzy Clustering7.2 Principles of Cluster Detection7.3 Some General Clustering Concepts7.4 Crisp Clustering Techniques7.5 Fuzzy Clustering Concepts7.6 Fuzzy c-Means Clustering7.7 Fuzzy Adaptive Clustering7.8 Generating Rule PrototypesReviewReferencesChapter 8. FUZZY RULE INDUCTION8.1 The Vocabulary of Rule Induction8.2 Rule Induction and Fuzzy Models8.3 The Rule Induction Algorithm8.4 The Model Building Methodology8.5 A Rule Induction and Model Building Example8.6 Measuring Model RobustnessReviewReferencesTechnical ImplementationExternal ControlsOrganization of the Knowledge BaseExecuting A Fuzzy RulePART THREE – EVOLUTIONARY STRATEGIESChapter 9. FUNDAMENTAL CONCEPTS OF GENETIC ALGORITHMS9.1 The Vocabulary of Genetic Algorithms9.2 Overview9.3 The Architecture of a Genetic AlgorithmReviewReferencesChapter 10. GENETIC RESOURCE SCHEDULING OPTIMIZATION10.1 The Vocabulary of Resource-Constrained Scheduling10.2 Some Terminology Issues10.3 Fundamentals10.4 Objective Functions and Constraints10.5 Bringing It All Together – Constraint Scheduling10.6 A Genetic Crew Scheduler Architecture10.7 Implementing and Executing the Crew Scheduler10.8 Topology Constraint Algorithms and Techniques10.9 Adaptive Parameter OptimizationReviewReferencesChapter 11. GENETIC TUNING OF FUZZY MODELS11.1 The Genetic Tuner Process11.2 Configuration Parameters11.3 Implementing and Running the Genetic Tuner11.4 Advanced Genetic Tuning IssuesReviewReferences