KnowledgeBased Clustering: From Data to Information GranulesISBN: 9780471469667
336 pages
January 2005

Description
 A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics
 Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible
 Includes illustrative material andwellknown experimentsto offer handson experience
Table of Contents
Preface.
1. Clustering and Fuzzy Clustering.
1. Introduction.
2. Basic Notions and Notation.
2.1 Types of Data.
2.2 Distance and Similarity.
3. Main Categories of Clustering Algorithms.
3.1 Hierarchical Clustering.
3.2 Objective Function – Based Clustering.
4. Clustering and Classification.
5. Fuzzy Clustering.
6. Cluster Validity.
7. Extensions of Objective FunctionBased Fuzzy Clustering.
7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy CVarieties.
7.2 Possibilistic Clustering.
7.3 Noise Clustering.
8. Self Organizing Maps and Fuzzy Objective Function Based Clustering.
9. Conclusions.
References.
2. Computing with Granular Information: Fuzzy Sets and Fuzzy Relations.
1. A Paradigm of Granular Computing: Information Granules and their Processing.
2. Fuzzy Sets as HumanCentric Information Granules.
3. Operations on Fuzzy Sets.
4. Fuzzy Relations.
5. Comparison of Two Fuzzy Sets.
6. Generalizations of Fuzzy Sets.
7. Shadowed Sets.
8. Rough Sets.
9. Granular Computing and Distributed Processing.
10. Conclusions.
References.
3. LogicOriented Neurocomputing.
1. Introduction.
2. Main Categories of Fuzzy Neurons.
2.1 Aggregative Neurons.
2.2 Referential (reference) Neurons.
3. Architectures of Logic Networks.
4. Interpretation Aspects of the Networks.
5. The Granular Interfaces of Logic Processing.
6. Conclusions.
References.
4. Conditional Fuzzy Clustering.
1. Introduction.
2. Problem Statement: Context Fuzzy Sets and Objective Function.
3. The Optimization Problem.
4. Computational Considerations of Conditional Clustering.
5. Generalizations of the Algorithm Through the Aggregation Operator.
6. Fuzzy Clustering with Spatial Constraints.
7. Conclusions.
References.
5. Clustering with Partial Supervision.
1. Introduction.
2. Problem Formulation.
3. The Design of the Clusters.
4. Experimental Examples.
5. ClusterBased Tracking Problem.
6. Conclusions.
References.
6. Principles of KnowledgeBased Guidance in Fuzzy Clustering.
1. Introduction.
2. Examples of KnowledgeOriented Hints and their General Taxonomy.
3. The Optimization Environment of KnowledgeEnhanced Clustering.
4. Quantification of KnowledgeBased Guidance Hints and Their Optimization.
5. The Organization of the Interaction Process.
6. Proximity – Based Clustering (PFCM).
7. Web Exploration and PFCM.
8. Linguistic Augmentation of KnowledgeBased Hints.
9. Concluding Comments.
References.
7. Collaborative Clustering.
1. Introduction and Rationale.
2. Horizontal and Vertical Clustering.
3. Horizontal Collaborative Clustering.
3.1 Optimization Details.
3.2 The Flow of Computing of Collaborative Clustering.
3.3 Quantification of the Collaborative Phenomenon of the Clustering.
4. Experimental Studies.
5. Further Enhancements of Horizontal Clustering.
6. The Algorithm of Vertical Clustering.
7. A Grid Model of Horizontal and Vertical Clustering.
8. Consensus Clustering.
9. Conclusions.
References.
8. Directional Clustering.
1. Introduction.
2. Problem Formulation.
2.1 The Objective Function.
2.2 The Logic Transformation Between Information Granules.
3. The Algorithm.
4. The Overall Development Framework of Directional Clustering.
5. Numerical Studies.
6. Conclusions.
References.
9. Fuzzy Relational Clustering.
1. Introduction and Problem Statement.
2. FCM for Relational Data.
3. Decomposition of Fuzzy Relational Patterns.
3.1 GradientBased Solution to the Decomposition Problem.
3.2 Neural Network Model of the Decomposition Problem.
4. Comparative Analysis.
5. Conclusions.
References.
10. Fuzzy Clustering of Heterogeneous Patterns.
1. Introduction.
2. Heterogeneous Data.
3. Parametric Models of Granular Data.
4. Parametric Mode of Heterogeneous Fuzzy Clustering.
5. Nonparametric Heterogeneous Clustering.
5.1 A Frame of Reference.
5.2 Representation of Granular Data Through the PossibilityNecessity Transformation.
5.3 Dereferencing.
6. Conclusions.
References.
11. Hyperbox Models of Granular Data: The Tchebyschev FCM.
1. Introduction.
2. Problem Formulation.
3. The Clustering AlgorithmDetailed Considerations.
4. The Development of Granular Prototypes.
5. The Geometry of Information Granules.
6. Granular Data Description: A General Model.
7. Conclusions.
References.
12. Genetic Tolerance Fuzzy Neural Networks.
1. Introduction.
2. Operations of Thresholdings and Tolerance: Fuzzy LogicBased Generalizations.
3. The Topology of the Logic Network.
4. Genetic Optimization.
5. Illustrative Numeric Studies.
6. Conclusions.
References.
13. Granular Prototyping.
1. Introduction.
2. Problem Formulation.
2.1 Expressing Similarity Between Two Fuzzy Sets.
2.2 Performance Index (objective function).
3. Prototype Optimization.
4. The Development of Granular Prototypes.
4.1 Optimization of the Similarity Levels.
4.2 An Inverse Similarity Problem.
5. Conclusions.
References.
14. Granular Mappings.
1. Introduction and Problem Statement.
2. Possibility and Necessity measure as the Computational Vehicle of Granular Representation.
3. Building the Granular Mapping.
4. The Design of Multivariable Granular Mappings Through Fuzzy Clustering.
5. Quantification of Granular Mappings.
6. Experimental Studies.
7. Conclusions.
References.
15. Linguistic Modeling.
1. Introduction.
2. The ClusterBased Representation of the Input – Output Mapping.
3. Conditional Clustering in the development of a blueprint of granular models.
4. Granular neuron as a Generic Processing Element in Granular Networks.
5. The Architecture of Linguistic Models Based on Conditional Fuzzy Clustering.
6. Refinements of Linguistic Models.
7. Conclusions.
References.
Bibliography.
Index.
Author Information
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