LEADER 05359nam 22007574a 450 001 9910806144503321 005 20200520144314.0 010 $a9786610275472 010 $a9781280275470 010 $a1280275472 010 $a9780470243558 010 $a0470243554 010 $a9780471708599 010 $a0471708593 010 $a9780471708605 010 $a0471708607 035 $a(CKB)1000000000019091 035 $a(EBL)228460 035 $a(OCoLC)609260264 035 $a(SSID)ssj0000188010 035 $a(PQKBManifestationID)11156586 035 $a(PQKBTitleCode)TC0000188010 035 $a(PQKBWorkID)10141617 035 $a(PQKB)10875312 035 $a(MiAaPQ)EBC228460 035 $a(Au-PeEL)EBL228460 035 $a(CaPaEBR)ebr10114191 035 $a(CaONFJC)MIL27547 035 $a(Perlego)2770481 035 $a(EXLCZ)991000000000019091 100 $a20040629d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge-based clustering $efrom data to information granules /$fWitold Pedrycz 205 $a1st ed. 210 $aHoboken, N.J. $cWiley$dc2005 215 $a1 online resource (336 p.) 300 $a"A Wiley-Interscience publication." 311 08$a9780471469667 311 08$a0471469661 320 $aIncludes bibliographical references (p. 297-313) and index. 327 $aKNOWLEDGE-BASED CLUSTERING; Contents; Foreword; Preface; 1 Clustering and Fuzzy Clustering; 1.1 Introduction; 1.2 Basic Notions and Notation; 1.2.1 Types of Data; 1.2.2 Distance and Similarity; 1.3 Main Categories of Clustering Algorithms; 1.3.1 Hierarchical Clustering; 1.3.2 Objective Function-Based Clustering; 1.4 Clustering and Classification; 1.5 Fuzzy Clustering; 1.6 Cluster Validity; 1.7 Extensions of Objective Function-Based Fuzzy Clustering; 1.7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C Varieties; 1.7.2 Possibilistic Clustering; 1.7.3 Noise Clustering 327 $a1.8 Self-Organizing Maps and Fuzzy Objective Function-Based Clustering1.9 Conclusions; References; 2 Computing with Granular Information: Fuzzy Sets and Fuzzy Relations; 2.1 A Paradigm of Granular Computing: Information Granules and Their Processing; 2.2 Fuzzy Sets as Human-Centric Information Granules; 2.3 Operations on Fuzzy Sets; 2.4 Fuzzy Relations; 2.5 Comparison of Two Fuzzy Sets; 2.6 Generalizations of Fuzzy Sets; 2.7 Shadowed Sets; 2.8 Rough Sets; 2.9 Granular Computing and Distributed Processing; 2.10 Conclusions; References; 3 Logic-Oriented Neurocomputing; 3.1 Introduction 327 $a3.2 Main Categories of Fuzzy Neurons3.2.1 Aggregative Neurons; 3.2.2 Referential (Reference) Neurons; 3.3 Architectures of Logic Networks; 3.4 Interpretation Aspects of the Networks; 3.5 Granular Interfaces of Logic Processing; 3.6 Conclusions; References; 4 Conditional Fuzzy Clustering; 4.1 Introduction; 4.2 Problem Statement: Context Fuzzy Sets and Objective Function; 4.3 The Optimization Problem; 4.4 Computational Considerations of Conditional Clustering; 4.5 Generalizations of the Algorithm Through the Aggregation Operator; 4.6 Fuzzy Clustering with Spatial Constraints; 4.7 Conclusions 327 $aReferences5 Clustering with Partial Supervision; 5.1 Introduction; 5.2 Problem Formulation; 5.3 Design of the Clusters; 5.4 Experimental Examples; 5.5 Cluster-Based Tracking Problem; 5.6 Conclusions; References; 6 Principles of Knowledge-Based Guidance in Fuzzy Clustering; 6.1 Introduction; 6.2 Examples of Knowledge-Oriented Hints and Their General Taxonomy; 6.3 The Optimization Environment of Knowledge-Enhanced Clustering; 6.4 Quantification of Knowledge-Based Guidance Hints and Their Optimization; 6.5 Organization of the Interaction Process; 6.6 Proximity-Based Clustering (P-FCM) 327 $a6.7 Web Exploration and P-FCM6.8 Linguistic Augmentation of Knowledge-Based Hints; 6.9 Conclusions; References; 7 Collaborative Clustering; 7.1 Introduction and Rationale; 7.2 Horizontal and Vertical Clustering; 7.3 Horizontal Collaborative Clustering; 7.3.1 Optimization Details; 7.3.2 The Flow of Computing of Collaborative Clustering; 7.3.3 Quantification of the Collaborative Phenomenon of Clustering; 7.4 Experimental Studies; 7.5 Further Enhancements of Horizontal Clustering; 7.6 The Algorithm of Vertical Clustering; 7.7 A Grid Model of Horizontal and Vertical Clustering 327 $a7.8 Consensus Clustering 330 $aA comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topicsCovers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangibleIncludes illustrative material andwell-known experimentsto offer hands-on experience 606 $aSoft computing 606 $aGranular computing 606 $aFuzzy systems 615 0$aSoft computing. 615 0$aGranular computing. 615 0$aFuzzy systems. 676 $a006.3 700 $aPedrycz$b Witold$f1953-$021029 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910806144503321 996 $aKnowledge-based clustering$94092360 997 $aUNINA