{"product_id":"knowledgebased-clustering-9780471469667","title":"KnowledgeBased Clustering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cul\u003e \u003cli\u003eA 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\u003c\/li\u003e \u003cli\u003eCovers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible\u003c\/li\u003e \u003cli\u003eIncludes illustrative material andwell-known experimentsto offer hands-on experience\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"I agree with Zadeh's opinion (mentioned at the end of book's foreword): 'The author and the publisher deserve our loud applause and congratulations.'\" (\u003ci\u003eComputing Reviews.com\u003c\/i\u003e, May 19, 2005)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eForeword.  \u003cp\u003ePreface.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Clustering and Fuzzy Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Basic Notions and Notation.\u003c\/p\u003e \u003cp\u003e2.1 Types of Data.\u003c\/p\u003e \u003cp\u003e2.2 Distance and Similarity.\u003c\/p\u003e \u003cp\u003e3. Main Categories of Clustering Algorithms.\u003c\/p\u003e \u003cp\u003e3.1 Hierarchical Clustering.\u003c\/p\u003e \u003cp\u003e3.2 Objective Function – Based Clustering.\u003c\/p\u003e \u003cp\u003e4. Clustering and Classification.\u003c\/p\u003e \u003cp\u003e5. Fuzzy Clustering.\u003c\/p\u003e \u003cp\u003e6. Cluster Validity.\u003c\/p\u003e \u003cp\u003e7. Extensions of Objective Function-Based Fuzzy Clustering.\u003c\/p\u003e \u003cp\u003e7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C-Varieties.\u003c\/p\u003e \u003cp\u003e7.2 Possibilistic Clustering.\u003c\/p\u003e \u003cp\u003e7.3 Noise Clustering.\u003c\/p\u003e \u003cp\u003e8. Self Organizing Maps and Fuzzy Objective Function Based Clustering.\u003c\/p\u003e \u003cp\u003e9. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Computing with Granular Information: Fuzzy Sets and Fuzzy Relations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. A Paradigm of Granular Computing: Information Granules and their Processing.\u003c\/p\u003e \u003cp\u003e2. Fuzzy Sets as Human-Centric Information Granules.\u003c\/p\u003e \u003cp\u003e3. Operations on Fuzzy Sets.\u003c\/p\u003e \u003cp\u003e4. Fuzzy Relations.\u003c\/p\u003e \u003cp\u003e5. Comparison of Two Fuzzy Sets.\u003c\/p\u003e \u003cp\u003e6. Generalizations of Fuzzy Sets.\u003c\/p\u003e \u003cp\u003e7. Shadowed Sets.\u003c\/p\u003e \u003cp\u003e8. Rough Sets.\u003c\/p\u003e \u003cp\u003e9. Granular Computing and Distributed Processing.\u003c\/p\u003e \u003cp\u003e10. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Logic-Oriented Neurocomputing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Main Categories of Fuzzy Neurons.\u003c\/p\u003e \u003cp\u003e2.1 Aggregative Neurons.\u003c\/p\u003e \u003cp\u003e2.2 Referential (reference) Neurons.\u003c\/p\u003e \u003cp\u003e3. Architectures of Logic Networks.\u003c\/p\u003e \u003cp\u003e4. Interpretation Aspects of the Networks.\u003c\/p\u003e \u003cp\u003e5. The Granular Interfaces of Logic Processing.\u003c\/p\u003e \u003cp\u003e6. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Conditional Fuzzy Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Problem Statement: Context Fuzzy Sets and Objective Function.\u003c\/p\u003e \u003cp\u003e3. The Optimization Problem.\u003c\/p\u003e \u003cp\u003e4. Computational Considerations of Conditional Clustering.\u003c\/p\u003e \u003cp\u003e5. Generalizations of the Algorithm Through the Aggregation Operator.\u003c\/p\u003e \u003cp\u003e6. Fuzzy Clustering with Spatial Constraints.\u003c\/p\u003e \u003cp\u003e7. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Clustering with Partial Supervision.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Problem Formulation.\u003c\/p\u003e \u003cp\u003e3. The Design of the Clusters.\u003c\/p\u003e \u003cp\u003e4. Experimental Examples.\u003c\/p\u003e \u003cp\u003e5. Cluster-Based Tracking Problem.\u003c\/p\u003e \u003cp\u003e6. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Principles of Knowledge-Based Guidance in Fuzzy Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Examples of Knowledge-Oriented Hints and their General Taxonomy.\u003c\/p\u003e \u003cp\u003e3. The Optimization Environment of Knowledge-Enhanced Clustering.\u003c\/p\u003e \u003cp\u003e4. Quantification of Knowledge-Based Guidance Hints and Their Optimization.\u003c\/p\u003e \u003cp\u003e5. The Organization of the Interaction Process.\u003c\/p\u003e \u003cp\u003e6. Proximity – Based Clustering (P-FCM).\u003c\/p\u003e \u003cp\u003e7. Web Exploration and P-FCM.\u003c\/p\u003e \u003cp\u003e8. Linguistic Augmentation of Knowledge-Based Hints.\u003c\/p\u003e \u003cp\u003e9. Concluding Comments.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Collaborative Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction and Rationale.\u003c\/p\u003e \u003cp\u003e2. Horizontal and Vertical Clustering.\u003c\/p\u003e \u003cp\u003e3. Horizontal Collaborative Clustering.\u003c\/p\u003e \u003cp\u003e3.1 Optimization Details.\u003c\/p\u003e \u003cp\u003e3.2 The Flow of Computing of Collaborative Clustering.\u003c\/p\u003e \u003cp\u003e3.3 Quantification of the Collaborative Phenomenon of the Clustering.\u003c\/p\u003e \u003cp\u003e4. Experimental Studies.\u003c\/p\u003e \u003cp\u003e5. Further Enhancements of Horizontal Clustering.\u003c\/p\u003e \u003cp\u003e6. The Algorithm of Vertical Clustering.\u003c\/p\u003e \u003cp\u003e7. A Grid Model of Horizontal and Vertical Clustering.\u003c\/p\u003e \u003cp\u003e8. Consensus Clustering.\u003c\/p\u003e \u003cp\u003e9. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Directional Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Problem Formulation.\u003c\/p\u003e \u003cp\u003e2.1 The Objective Function.\u003c\/p\u003e \u003cp\u003e2.2 The Logic Transformation Between Information Granules.\u003c\/p\u003e \u003cp\u003e3. The Algorithm.\u003c\/p\u003e \u003cp\u003e4. The Overall Development Framework of Directional Clustering.\u003c\/p\u003e \u003cp\u003e5. Numerical Studies.\u003c\/p\u003e \u003cp\u003e6. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Fuzzy Relational Clustering.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction and Problem Statement.\u003c\/p\u003e \u003cp\u003e2. FCM for Relational Data.\u003c\/p\u003e \u003cp\u003e3. Decomposition of Fuzzy Relational Patterns.\u003c\/p\u003e \u003cp\u003e3.1 Gradient-Based Solution to the Decomposition Problem.\u003c\/p\u003e \u003cp\u003e3.2 Neural Network Model of the Decomposition Problem.\u003c\/p\u003e \u003cp\u003e4. Comparative Analysis.\u003c\/p\u003e \u003cp\u003e5. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Fuzzy Clustering of Heterogeneous Patterns.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Heterogeneous Data.\u003c\/p\u003e \u003cp\u003e3. Parametric Models of Granular Data.\u003c\/p\u003e \u003cp\u003e4. Parametric Mode of Heterogeneous Fuzzy Clustering.\u003c\/p\u003e \u003cp\u003e5. Nonparametric Heterogeneous Clustering.\u003c\/p\u003e \u003cp\u003e5.1 A Frame of Reference.\u003c\/p\u003e \u003cp\u003e5.2 Representation of Granular Data Through the Possibility-Necessity Transformation.\u003c\/p\u003e \u003cp\u003e5.3 Dereferencing.\u003c\/p\u003e \u003cp\u003e6. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Hyperbox Models of Granular Data: The Tchebyschev FCM.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Problem Formulation.\u003c\/p\u003e \u003cp\u003e3. The Clustering Algorithm-Detailed Considerations.\u003c\/p\u003e \u003cp\u003e4. The Development of Granular Prototypes.\u003c\/p\u003e \u003cp\u003e5. The Geometry of Information Granules.\u003c\/p\u003e \u003cp\u003e6. Granular Data Description: A General Model.\u003c\/p\u003e \u003cp\u003e7. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Genetic Tolerance Fuzzy Neural Networks.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Operations of Thresholdings and Tolerance: Fuzzy Logic-Based Generalizations.\u003c\/p\u003e \u003cp\u003e3. The Topology of the Logic Network.\u003c\/p\u003e \u003cp\u003e4. Genetic Optimization.\u003c\/p\u003e \u003cp\u003e5. Illustrative Numeric Studies.\u003c\/p\u003e \u003cp\u003e6. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Granular Prototyping.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. Problem Formulation.\u003c\/p\u003e \u003cp\u003e2.1 Expressing Similarity Between Two Fuzzy Sets.\u003c\/p\u003e \u003cp\u003e2.2 Performance Index (objective function).\u003c\/p\u003e \u003cp\u003e3. Prototype Optimization.\u003c\/p\u003e \u003cp\u003e4. The Development of Granular Prototypes.\u003c\/p\u003e \u003cp\u003e4.1 Optimization of the Similarity Levels.\u003c\/p\u003e \u003cp\u003e4.2 An Inverse Similarity Problem.\u003c\/p\u003e \u003cp\u003e5. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Granular Mappings.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction and Problem Statement.\u003c\/p\u003e \u003cp\u003e2. Possibility and Necessity measure as the Computational Vehicle of Granular Representation.\u003c\/p\u003e \u003cp\u003e3. Building the Granular Mapping.\u003c\/p\u003e \u003cp\u003e4. The Design of Multivariable Granular Mappings Through Fuzzy Clustering.\u003c\/p\u003e \u003cp\u003e5. Quantification of Granular Mappings.\u003c\/p\u003e \u003cp\u003e6. Experimental Studies.\u003c\/p\u003e \u003cp\u003e7. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Linguistic Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1. Introduction.\u003c\/p\u003e \u003cp\u003e2. The Cluster-Based Representation of the Input – Output Mapping.\u003c\/p\u003e \u003cp\u003e3. Conditional Clustering in the development of a blueprint of granular models.\u003c\/p\u003e \u003cp\u003e4. Granular neuron as a Generic Processing Element in Granular Networks.\u003c\/p\u003e \u003cp\u003e5. The Architecture of Linguistic Models Based on Conditional Fuzzy Clustering.\u003c\/p\u003e \u003cp\u003e6. Refinements of Linguistic Models.\u003c\/p\u003e \u003cp\u003e7. Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402604519767,"sku":"9780471469667","price":107.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471469667.jpg?v=1730480930","url":"https:\/\/bookcurl.com\/products\/knowledgebased-clustering-9780471469667","provider":"Book Curl","version":"1.0","type":"link"}