Front Cover -- Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration -- Copyright Page -- Contents -- Preface -- Objectives and Audience -- Organization of the Book -- Algorithm Definitions and Examples -- Acknowledgments -- Introduction -- The Modern Connected World -- The Advent of Intelligent Models -- Fuzzy Logic and Genetic Algorithms -- Part I: Concepts and Issues -- Chapter 1. Foundations and Ideas -- 1.1 Enterprise Applications and Analysis Models -- 1.2 Distributed and Centralized Repositories -- 1.3 The Age of Distributed Knowledge -- 1.4 Information and Knowledge Discovery -- 1.5 Data Mining and Business Models -- 1.6 Fuzzy Systems for Business Process Models -- 1.7 Evolving Distributed Fuzzy Models -- 1.8 A Sample Case: Evolving a Model for Customer Segmentation -- 1.9 Review -- Chapter 2. Principal Model Types -- 2.1 Model and Event State Categorization -- 2.2 Model Type and Outcome Categorization -- 2.3 Review -- Chapter 3. Approaches to Model Building -- 3.1 Ordinary Statistics -- 3.2 Nonparametric Statistics -- 3.3 Linear Regression in Statistical Models -- 3.4 Nonlinear Growth Curve Fitting -- 3.5 Cluster Analysis -- 3.6 Decision Trees and Classifiers -- 3.7 Neural Networks -- 3.8 Fuzzy SQL Systems -- 3.9 Rule Induction and Dynamic Fuzzy Models -- 3.10 Review -- Further Reading -- Part II: Fuzzy Systems -- Chapter 4. Fundamental Concepts of Fuzzy Logic -- 4.1 The Vocabulary of Fuzzy Logic -- 4.2 Boolean (Crisp) Sets: The Law of Bivalence -- 4.3 Fuzzy Sets -- 4.4 Review -- Chapter 5. Fundamental Concepts of Fuzzy Systems -- 5.1 The Vocabulary of Fuzzy Systems -- 5.2 Fuzzy Rule-based Systems: An Overview -- 5.3 Variable Decomposition into Fuzzy Sets -- 5.4 A Fuzzy Knowledge Base: The Details -- 5.5 The Fuzzy Inference Engine -- 5.6 Inference Engine Approaches -- 5.7 Running a Fuzzy Model -- 5.8 Review.

Chapter 6. Fuzzy SQL and Intelligent Queries -- 6.1 The Vocabulary of Relational Databases and Queries -- 6.2 Basic Relational Database Concepts -- 6.3 Structured Query Language Fundamentals -- 6.4 Precision and Accuracy -- 6.5 Why We Search Databases -- 6.6 Expanding the Query Scope -- 6.7 Fuzzy Query Fundamentals -- 6.8 Measuring Query Compatibility -- 6.9 Complex Query Compatibility Metrics -- 6.10 Compatibility Threshold Management -- 6.11 Fuzzy SQL Process Flow -- 6.12 Fuzzy SQL Example -- 6.13 Evaluating Fuzzy SQL Outcomes -- 6.14 Review -- Chapter 7. Fuzzy Clustering -- 7.1 The Vocabulary of Fuzzy Clustering -- 7.2 Principles of Cluster Detection -- 7.3 Some General Clustering Concepts -- 7.4 Crisp Clustering Techniques -- 7.5 Fuzzy Clustering Concepts -- 7.6 Fuzzy c-Means Clustering -- 7.7 Fuzzy Adaptive Clustering -- 7.8 Generating Rule Prototypes -- 7.9 Review -- Chapter 8. Fuzzy Rule Induction -- 8.1 The Vocabulary of Rule Induction -- 8.2 Rule Induction and Fuzzy Models -- 8.3 The Rule Induction Algorithm -- 8.4 The Model Building Methodology -- 8.5 A Rule Induction and Model Building Example -- 8.6 Measuring Model Robustness -- 8.7 Technical Implementation -- 8.8 External Controls -- 8.9 Organization of the Knowledge Base -- 8.10 Review -- Part III: Evolutionary Strategies -- Chapter 9. Fundamental Concepts of Genetic Algorithms -- 9.1 The Vocabulary of Genetic Algorithms -- 9.2 Overview -- 9.3 The Architecture of a Genetic Algorithm -- 9.4 Practical Issues in Using a Genetic Algorithm -- 9.5 Review -- Chapter 10. Genetic Resource Scheduling Optimization -- 10.1 The Vocabulary of Resource-constrained Scheduling -- 10.2 Some Terminology Issues -- 10.3 Fundamentals -- 10.4 Objective Functions and Constraints -- 10.5 Bringing It All Together: Constraint Scheduling -- 10.6 A Genetic Crew Scheduler Architecture.

10.7 Implementing and Executing the Crew Scheduler -- 10.8 Topology Constraint Algorithms and Techniques -- 10.9 Adaptive Parameter Optimization -- 10.10 Review -- Chapter 11. Genetic Tuning of Fuzzy Models -- 11.1 The Genetic Tuner Process -- 11.2 Configuration Parameters -- 11.3 Implementing and Running the Genetic Tuner -- 11.4 Advanced Genetic Tuning Issues -- 11.5 Review -- Index.