01496nam 2200469 450 991015953750332120230803035416.01-62387-411-4(CKB)3710000000264894(EBL)1817202(SSID)ssj0001399615(PQKBManifestationID)11867892(PQKBTitleCode)TC0001399615(PQKBWorkID)11452028(PQKB)10996348(MiAaPQ)EBC1817202(EXLCZ)99371000000026489420141031h20132013 uy 0engur|n|---|||||txtccrAprendendo a pensar sobre raiva uma história sobre raiva /Salem de Bezenac[Paris, France] :[iCharacter.org],[2013]©[2013]1 online resource (32 p.)Description based upon print version of record. Feeling angry is normal, but Rita gives us an example of how she learned to handle that emotion appropriately. Suggested for ages 6 and younger. AngerJuvenile literatureAngerMoral and ethical aspectsAngerAngerMoral and ethical aspects.152.47Bezenac Salem de1076923MiAaPQMiAaPQMiAaPQBOOK9910159537503321Aprendendo a pensar sobre raiva3416360UNINA05359nam 22007574a 450 991080614450332120200520144314.0978661027547297812802754701280275472978047024355804702435549780471708599047170859397804717086050471708607(CKB)1000000000019091(EBL)228460(OCoLC)609260264(SSID)ssj0000188010(PQKBManifestationID)11156586(PQKBTitleCode)TC0000188010(PQKBWorkID)10141617(PQKB)10875312(MiAaPQ)EBC228460(Au-PeEL)EBL228460(CaPaEBR)ebr10114191(CaONFJC)MIL27547(Perlego)2770481(EXLCZ)99100000000001909120040629d2005 uy 0engur|n|---|||||txtccrKnowledge-based clustering from data to information granules /Witold Pedrycz1st ed.Hoboken, N.J. Wileyc20051 online resource (336 p.)"A Wiley-Interscience publication."9780471469667 0471469661 Includes bibliographical references (p. 297-313) and index.KNOWLEDGE-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 Clustering1.8 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 Introduction3.2 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 ConclusionsReferences5 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)6.7 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 Clustering7.8 Consensus ClusteringA 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 experienceSoft computingGranular computingFuzzy systemsSoft computing.Granular computing.Fuzzy systems.006.3Pedrycz Witold1953-21029MiAaPQMiAaPQMiAaPQBOOK9910806144503321Knowledge-based clustering4092360UNINA