Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

by Earl Cox
Released February 2005
Publisher(s): Morgan Kaufmann
ISBN: 9780080470597

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Book description

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.

  • Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems
  • Helps you to understand the trade-offs implicit in various models and model architectures
  • Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction
  • Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model
  • In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem
  • Presents examples in C, C++, Java, and easy-to-understand pseudo-code
  • Extensive online component, including sample code and a complete data mining workbench

Table of contents

  1. Front Cover
  2. Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
  3. Copyright Page
  4. Contents (1/2)
  5. Contents (2/2)
  6. Preface
    1. Objectives and Audience
    2. Organization of the Book
    3. Algorithm Definitions and Examples
  7. Acknowledgments
  8. Introduction
    1. The Modern Connected World
    2. The Advent of Intelligent Models
    3. Fuzzy Logic and Genetic Algorithms
  9. Part I: Concepts and Issues
    1. Chapter 1. Foundations and Ideas
      1. 1.1 Enterprise Applications and Analysis Models
      2. 1.2 Distributed and Centralized Repositories
      3. 1.3 The Age of Distributed Knowledge
      4. 1.4 Information and Knowledge Discovery (1/2)
      5. 1.4 Information and Knowledge Discovery (2/2)
      6. 1.5 Data Mining and Business Models
      7. 1.6 Fuzzy Systems for Business Process Models
      8. 1.7 Evolving Distributed Fuzzy Models
      9. 1.8 A Sample Case: Evolving a Model for Customer Segmentation
      10. 1.9 Review
    2. Chapter 2. Principal Model Types
      1. 2.1 Model and Event State Categorization
      2. 2.2 Model Type and Outcome Categorization
      3. 2.3 Review
    3. Chapter 3. Approaches to Model Building
      1. 3.1 Ordinary Statistics
      2. 3.2 Nonparametric Statistics
      3. 3.3 Linear Regression in Statistical Models
      4. 3.4 Nonlinear Growth Curve Fitting
      5. 3.5 Cluster Analysis
      6. 3.6 Decision Trees and Classifiers
      7. 3.7 Neural Networks
      8. 3.8 Fuzzy SQL Systems
      9. 3.9 Rule Induction and Dynamic Fuzzy Models (1/2)
      10. 3.9 Rule Induction and Dynamic Fuzzy Models (2/2)
      11. 3.10 Review
      12. Further Reading
  10. Part II: Fuzzy Systems
    1. Chapter 4. Fundamental Concepts of Fuzzy Logic
      1. 4.1 The Vocabulary of Fuzzy Logic
      2. 4.2 Boolean (Crisp) Sets: The Law of Bivalence
      3. 4.3 Fuzzy Sets (1/4)
      4. 4.3 Fuzzy Sets (2/4)
      5. 4.3 Fuzzy Sets (3/4)
      6. 4.3 Fuzzy Sets (4/4)
      7. 4.4 Review
    2. Chapter 5. Fundamental Concepts of Fuzzy Systems
      1. 5.1 The Vocabulary of Fuzzy Systems
      2. 5.2 Fuzzy Rule-based Systems: An Overview
      3. 5.3 Variable Decomposition into Fuzzy Sets (1/2)
      4. 5.3 Variable Decomposition into Fuzzy Sets (2/2)
      5. 5.4 A Fuzzy Knowledge Base: The Details (1/2)
      6. 5.4 A Fuzzy Knowledge Base: The Details (2/2)
      7. 5.5 The Fuzzy Inference Engine
      8. 5.6 Inference Engine Approaches
      9. 5.7 Running a Fuzzy Model (1/5)
      10. 5.7 Running a Fuzzy Model (2/5)
      11. 5.7 Running a Fuzzy Model (3/5)
      12. 5.7 Running a Fuzzy Model (4/5)
      13. 5.7 Running a Fuzzy Model (5/5)
      14. 5.8 Review
    3. Chapter 6. Fuzzy SQL and Intelligent Queries
      1. 6.1 The Vocabulary of Relational Databases and Queries (1/2)
      2. 6.1 The Vocabulary of Relational Databases and Queries (2/2)
      3. 6.2 Basic Relational Database Concepts
      4. 6.3 Structured Query Language Fundamentals
      5. 6.4 Precision and Accuracy
      6. 6.5 Why We Search Databases
      7. 6.6 Expanding the Query Scope
      8. 6.7 Fuzzy Query Fundamentals (1/3)
      9. 6.7 Fuzzy Query Fundamentals (2/3)
      10. 6.7 Fuzzy Query Fundamentals (3/3)
      11. 6.8 Measuring Query Compatibility
      12. 6.9 Complex Query Compatibility Metrics
      13. 6.10 Compatibility Threshold Management
      14. 6.11 Fuzzy SQL Process Flow
      15. 6.12 Fuzzy SQL Example (1/2)
      16. 6.12 Fuzzy SQL Example (2/2)
      17. 6.13 Evaluating Fuzzy SQL Outcomes
      18. 6.14 Review
    4. Chapter 7. Fuzzy Clustering
      1. 7.1 The Vocabulary of Fuzzy Clustering
      2. 7.2 Principles of Cluster Detection
      3. 7.3 Some General Clustering Concepts (1/2)
      4. 7.3 Some General Clustering Concepts (2/2)
      5. 7.4 Crisp Clustering Techniques
      6. 7.5 Fuzzy Clustering Concepts
      7. 7.6 Fuzzy c-Means Clustering (1/4)
      8. 7.6 Fuzzy c-Means Clustering (2/4)
      9. 7.6 Fuzzy c-Means Clustering (3/4)
      10. 7.6 Fuzzy c-Means Clustering (4/4)
      11. 7.7 Fuzzy Adaptive Clustering (1/3)
      12. 7.7 Fuzzy Adaptive Clustering (2/3)
      13. 7.7 Fuzzy Adaptive Clustering (3/3)
      14. 7.8 Generating Rule Prototypes
      15. 7.9 Review
    5. Chapter 8. Fuzzy Rule Induction
      1. 8.1 The Vocabulary of Rule Induction
      2. 8.2 Rule Induction and Fuzzy Models
      3. 8.3 The Rule Induction Algorithm (1/2)
      4. 8.3 The Rule Induction Algorithm (2/2)
      5. 8.4 The Model Building Methodology
      6. 8.5 A Rule Induction and Model Building Example (1/5)
      7. 8.5 A Rule Induction and Model Building Example (2/5)
      8. 8.5 A Rule Induction and Model Building Example (3/5)
      9. 8.5 A Rule Induction and Model Building Example (4/5)
      10. 8.5 A Rule Induction and Model Building Example (5/5)
      11. 8.6 Measuring Model Robustness (1/3)
      12. 8.6 Measuring Model Robustness (2/3)
      13. 8.6 Measuring Model Robustness (3/3)
      14. 8.7 Technical Implementation
      15. 8.8 External Controls (1/2)
      16. 8.8 External Controls (2/2)
      17. 8.9 Organization of the Knowledge Base
      18. 8.10 Review
  11. Part III: Evolutionary Strategies
    1. Chapter 9. Fundamental Concepts of Genetic Algorithms
      1. 9.1 The Vocabulary of Genetic Algorithms (1/2)
      2. 9.1 The Vocabulary of Genetic Algorithms (2/2)
      3. 9.2 Overview (1/3)
      4. 9.2 Overview (2/3)
      5. 9.2 Overview (3/3)
      6. 9.3 The Architecture of a Genetic Algorithm (1/10)
      7. 9.3 The Architecture of a Genetic Algorithm (2/10)
      8. 9.3 The Architecture of a Genetic Algorithm (3/10)
      9. 9.3 The Architecture of a Genetic Algorithm (4/10)
      10. 9.3 The Architecture of a Genetic Algorithm (5/10)
      11. 9.3 The Architecture of a Genetic Algorithm (6/10)
      12. 9.3 The Architecture of a Genetic Algorithm (7/10)
      13. 9.3 The Architecture of a Genetic Algorithm (8/10)
      14. 9.3 The Architecture of a Genetic Algorithm (9/10)
      15. 9.3 The Architecture of a Genetic Algorithm (10/10)
      16. 9.4 Practical Issues in Using a Genetic Algorithm
      17. 9.5 Review
    2. Chapter 10. Genetic Resource Scheduling Optimization
      1. 10.1 The Vocabulary of Resource-constrained Scheduling
      2. 10.2 Some Terminology Issues
      3. 10.3 Fundamentals
      4. 10.4 Objective Functions and Constraints (1/2)
      5. 10.4 Objective Functions and Constraints (2/2)
      6. 10.5 Bringing It All Together: Constraint Scheduling (1/2)
      7. 10.5 Bringing It All Together: Constraint Scheduling (2/2)
      8. 10.6 A Genetic Crew Scheduler Architecture
      9. 10.7 Implementing and Executing the Crew Scheduler (1/4)
      10. 10.7 Implementing and Executing the Crew Scheduler (2/4)
      11. 10.7 Implementing and Executing the Crew Scheduler (3/4)
      12. 10.7 Implementing and Executing the Crew Scheduler (4/4)
      13. 10.8 Topology Constraint Algorithms and Techniques (1/3)
      14. 10.8 Topology Constraint Algorithms and Techniques (2/3)
      15. 10.8 Topology Constraint Algorithms and Techniques (3/3)
      16. 10.9 Adaptive Parameter Optimization
      17. 10.10 Review
    3. Chapter 11. Genetic Tuning of Fuzzy Models
      1. 11.1 The Genetic Tuner Process
      2. 11.2 Configuration Parameters (1/2)
      3. 11.2 Configuration Parameters (2/2)
      4. 11.3 Implementing and Running the Genetic Tuner (1/3)
      5. 11.3 Implementing and Running the Genetic Tuner (2/3)
      6. 11.3 Implementing and Running the Genetic Tuner (3/3)
      7. 11.4 Advanced Genetic Tuning Issues (1/2)
      8. 11.4 Advanced Genetic Tuning Issues (2/2)
      9. 11.5 Review
  12. Index (1/3)
  13. Index (2/3)
  14. Index (3/3)

Product information

  • Title: Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
  • Author(s): Earl Cox
  • Release date: February 2005
  • Publisher(s): Morgan Kaufmann
  • ISBN: 9780080470597