LEADER 05244nam 2200625Ia 450 001 9910830897803321 005 20230721030030.0 010 $a1-280-90081-4 010 $a9786610900817 010 $a0-470-06119-7 010 $a0-470-06118-9 035 $a(CKB)1000000000357115 035 $a(EBL)297463 035 $a(SSID)ssj0000098553 035 $a(PQKBManifestationID)11113147 035 $a(PQKBTitleCode)TC0000098553 035 $a(PQKBWorkID)10133032 035 $a(PQKB)10175065 035 $a(MiAaPQ)EBC297463 035 $a(OCoLC)181345541 035 $a(EXLCZ)991000000000357115 100 $a20061031d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvances in fuzzy clustering and its applications$b[electronic resource] /$fedited by J. Valente de Oliveira, W. Pedrycz 210 $aChichester $cWiley$dc2007 215 $a1 online resource (456 p.) 300 $aDescription based upon print version of record. 311 $a0-470-02760-6 320 $aIncludes bibliographical references and index. 327 $aAdvances in Fuzzy Clustering and its Applications; Contents; List of Contributors; Foreword; Preface; Part I Fundamentals 1; 1 Fundamentals of Fuzzy Clustering; 1.1 Introduction; 1.2 Basic Clustering Algorithms; 1.3 Distance Function Variants; 1.4 Objective Function Variants; 1.5 Update Equation Variants: Alternating Cluster Estimation; 1.6 Concluding Remarks; Acknowledgements; References; 2 Relational Fuzzy Clustering; 2.1 Introduction; 2.2 Object and Relational Data; 2.3 Object Data Clustering Models; 2.4 Relational Clustering; 2.5 Relational Clustering with Non-spherical Prototypes 327 $a2.6 Relational Data Interpreted as Object Data2.7 Summary; 2.8 Experiments; 2.9 Conclusions; References; 3 Fuzzy Clustering with Minkowski Distance Functions; 3.1 Introduction; 3.2 Formalization; 3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances; 3.4 The Effects of the Robustness Parameterl; 3.5 Internet Attitudes; 3.6 Conclusions; References; 4 Soft Cluster Ensembles; 4.1 Introduction; 4.2 Cluster Ensembles; 4.3 Soft Cluster Ensembles; 4.4 Experimental Setup; 4.5 Soft vs. Hard Cluster Ensembles; 4.6 Conclusions and Future Work; Acknowledgements; References 327 $aPart II Visualization5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures; 5.1 Problem Definition; 5.2 Classical Methods for Cluster Validity and Merging; 5.3 Similarity of Fuzzy Clusters; 5.4 Visualization of Clustering Results; 5.5 Conclusions; Appendix 5A.1 Validity Indices; Appendix 5A.2 The Modified Sammon Mapping Algorithm; Acknowledgements; References; 6 Interactive Exploration of Fuzzy Clusters; 6.1 Introduction; 6.2 Neighborgram Clustering; 6.3 Interactive Exploration; 6.4 Parallel Universes; 6.5 Discussion; References 327 $aPart III Algorithms and Computational Aspects7 Fuzzy Clustering with Participatory Learning and Applications; 7.1 Introduction; 7.2 Participatory Learning; 7.3 Participatory Learning in Fuzzy Clustering; 7.4 Experimental Results; 7.5 Applications; 7.6 Conclusions; Acknowledgements; References; 8 Fuzzy Clustering of Fuzzy Data; 8.1 Introduction; 8.2 Informational Paradigm, Fuzziness and Complexity in Clustering Processes; 8.3 Fuzzy Data; 8.4 Fuzzy Clustering of Fuzzy Data; 8.5 An Extension: Fuzzy Clustering Models for Fuzzy Data Time Arrays; 8.6 Applicative Examples 327 $a8.7 Concluding Remarks and Future PerspectivesReferences; 9 Inclusion-based Fuzzy Clustering; 9.1 Introduction; 9.2 Background: Fuzzy Clustering; 9.3 Construction of an Inclusion Index; 9.4 Inclusion-based Fuzzy Clustering; 9.5 Numerical Examples and Illustrations; 9.6 Conclusion; Acknowledgements; Appendix 9A.1; References; 10 Mining Diagnostic Rules Using Fuzzy Clustering; 10.1 Introduction; 10.2 Fuzzy Medical Diagnosis; 10.3 Interpretability in Fuzzy Medical Diagnosis; 10.4 A Framework for Mining Interpretable Diagnostic Rules; 10.5 An Illustrative Example; 10.6 Conclusive Remarks 327 $aReferences 330 $aA comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering. Fuzzy clustering is now a mature and vibrant area of research with highly innovative advanced applications. Encapsulating this through presenting a careful selection of research contributions, this book addresses timely and relevant concepts and methods, whilst identifying major challenges and recent developments in the area. Split into five clear sections, Fundamentals, Visualization, Algorithms and Computational Aspects, Real-Time and Dynamic Clustering, and Applications and Case Studies 606 $aFuzzy systems 606 $aSoft computing 615 0$aFuzzy systems. 615 0$aSoft computing. 676 $a006.3 686 $a54.72$2bcl 701 $aOliveira$b J. Valente de$g(Jose? Valente)$01649005 701 $aPedrycz$b Witold$f1953-$021029 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830897803321 996 $aAdvances in fuzzy clustering and its applications$93997504 997 $aUNINA