Fuzzy modeling and genetic algorithms for data mining and exploration
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...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
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San Francisco, CA :
Elsevier/Morgan Kaufmann
c2005.
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| Edición: | 1st edition |
| Colección: | Morgan Kaufmann series in data management systems.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627129406719 |
Tabla de Contenidos:
- 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 Models1.6 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 Systems3.9 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 Queries6.1 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 Detection7.3 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
