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Fuzzy modeling and genetic algorithms for data mining and exploration [[electronic resource] /] / Earl Cox
| Fuzzy modeling and genetic algorithms for data mining and exploration [[electronic resource] /] / Earl Cox |
| Autore | Cox Earl |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | San Francisco, CA, : Elsevier/Morgan Kaufmann, c2005 |
| Descrizione fisica | 1 online resource (553 p.) |
| Disciplina | 006.3/12 |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico |
Data mining
Fuzzy logic Genetic algorithms |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-96129-5
9786610961290 0-08-047059-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910458702303321 |
Cox Earl
|
||
| San Francisco, CA, : Elsevier/Morgan Kaufmann, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fuzzy modeling and genetic algorithms for data mining and exploration [[electronic resource] /] / Earl Cox
| Fuzzy modeling and genetic algorithms for data mining and exploration [[electronic resource] /] / Earl Cox |
| Autore | Cox Earl |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | San Francisco, CA, : Elsevier/Morgan Kaufmann, c2005 |
| Descrizione fisica | 1 online resource (553 p.) |
| Disciplina | 006.3/12 |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico |
Data mining
Fuzzy logic Genetic algorithms |
| ISBN |
1-280-96129-5
9786610961290 0-08-047059-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910784647003321 |
|
Cox Earl
|
||
| San Francisco, CA, : Elsevier/Morgan Kaufmann, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fuzzy modeling and genetic algorithms for data mining and exploration / / Earl Cox
| Fuzzy modeling and genetic algorithms for data mining and exploration / / Earl Cox |
| Autore | Cox Earl |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | San Francisco, CA, : Elsevier/Morgan Kaufmann, c2005 |
| Descrizione fisica | 1 online resource (553 p.) |
| Disciplina |
006.3/12
005.741 |
| Collana | The Morgan Kaufmann series in data management systems |
| Soggetto topico |
Data mining
Fuzzy logic Genetic algorithms |
| ISBN |
1-280-96129-5
9786610961290 0-08-047059-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910825426603321 |
|
Cox Earl
|
||
| San Francisco, CA, : Elsevier/Morgan Kaufmann, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||