Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration
- 1st Edition - January 18, 2005
- Latest edition
- Author: Earl Cox
- Language: English
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… Read more
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
Researchers and technicians in organisations with large databases.
Preface
Acknowledgements
Introduction
PART ONE – 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
Review
Chapter 2. PRINCIPAL MODEL TYPES
2.1 Model and Event State Categorization
2.2 Model Type and Outcome Categorization
Review
Chapter 3. APPROACHES TO MODEL BUILDING
3.1 Ordinary Statistics.
3.2 Non-Parametric Statistics
3.3 Linear Regression In Statistical Models
3.4 Non-Linear 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
Review
References
PART TWO – 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
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 Fuzzy Rules
5.4 Variable Decomposition Into Fuzzy Sets
5.5 A Fuzzy Knowledge Base – The Details
5.6 The Fuzzy Inference Engine
5.7 Inference Engine Approaches
5.8 Running A Fuzzy Model
Review
Chapter 6. FUZZYSQL 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 do we search a database?
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 FuzzySQL Process Flow
6.12 FuzzySQL Example
6.13 Evaluating the FuzzySQL Outcomes
Review
References
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
Review
References
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
Review
References
Technical Implementation
External Controls
Organization of the Knowledge Base
Executing A Fuzzy Rule
PART THREE – 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
Review
References
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
Review
References
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
Review
References
Acknowledgements
Introduction
PART ONE – 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
Review
Chapter 2. PRINCIPAL MODEL TYPES
2.1 Model and Event State Categorization
2.2 Model Type and Outcome Categorization
Review
Chapter 3. APPROACHES TO MODEL BUILDING
3.1 Ordinary Statistics.
3.2 Non-Parametric Statistics
3.3 Linear Regression In Statistical Models
3.4 Non-Linear 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
Review
References
PART TWO – 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
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 Fuzzy Rules
5.4 Variable Decomposition Into Fuzzy Sets
5.5 A Fuzzy Knowledge Base – The Details
5.6 The Fuzzy Inference Engine
5.7 Inference Engine Approaches
5.8 Running A Fuzzy Model
Review
Chapter 6. FUZZYSQL 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 do we search a database?
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 FuzzySQL Process Flow
6.12 FuzzySQL Example
6.13 Evaluating the FuzzySQL Outcomes
Review
References
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
Review
References
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
Review
References
Technical Implementation
External Controls
Organization of the Knowledge Base
Executing A Fuzzy Rule
PART THREE – 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
Review
References
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
Review
References
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
Review
References
- Edition: 1
- Latest edition
- Published: January 18, 2005
- Language: English
EC
Earl Cox
Earl founded and serves as President of, Scianta Intelligence, a next generation machine intelligence and knowledge exploration company. He is a futurist, author, management consultant, and educator involved in discovering the epistemology of advanced intelligent systems, the redefinition of the machine mind, and, as a pioneer of Internet-based technologies, the way in which evolving inter-connected virtual worlds will affect the sociology of business and culture in the near and far future.
Earl has over thirty years experience in managing and participating in the software development process at the system as well as tightly integrated application level. In the area of advanced machine intelligence technologies, Earl is a recognized expert in fuzzy logic, and adaptive fuzzy systems as they are applied to information and decision theory. He has pioneered the integration of fuzzy neural systems with genetic algorithms and case-based reasoning. As an industry observer and futurist, Earl has written and talked extensively on the philosophy of the Response to Change, the nature of Emergent Intelligence, and the Meaning of Information Entropy in Mind and Machine.
Affiliations and expertise
President, Scianta Intelligence, LLC, Chapel Hill, NC, USARead Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration on ScienceDirect