Title Advances in fuzzy clustering and its applications / edited by J. Valente de Oliveira, W. Pedrycz

Published Chichester : Wiley, [2007] ©2007

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Description xx, 434 pages : illustrations ; 24 cm Series Wiley InterScience Contents Machine derived contents note: List of Contributors. -- Foreword. -- Preface. -- Part I Fundamentals. -- 1 Fundamentals of Fuzzy Clustering (Rudolf Kruse, Christian Döring and Marie-Jeanne Lesot). -- 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 (Thomas A. Runkler). -- 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. -- 2.6 Relational Data Interpreted as Object Data. -- 2.7 Summary. -- 2.8 Experiments. -- 2.9 Conclusions. -- References. -- 3 Fuzzy Clustering with Minkowski Distance Functions (Patrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen). -- 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 Parameter. -- 3.5 Internet Attitudes. -- 3.6 Conclusions. -- References. -- 4 Soft Cluster Ensembles (Kunal Punera and Joydeep Ghosh). -- 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. -- Part II Visualization. -- 5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures (János Abonyi and Balázs Feil). -- 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 (Bernd Wiswedel, David E. Patterson and Michael R. Berthold). -- 6.1 Introduction. -- 6.2 Neighborgram Clustering. -- 6.3 Interactive Exploration. -- 6.4 Parallel Universes. -- 6.5 Discussion. -- References. -- Part III Algorithms and Computational Aspects. -- 7 Fuzzy Clustering with Participatory Learning and Applications (Leila Roling Scariot da Silva, Fernando Gomide and Ronald Yager). -- 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 (Pierpaolo D'Urso). -- 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. -- 8.7 Concluding Remarks and Future Perspectives. -- References. -- 9 Inclusion-based Fuzzy Clustering (Samia Nefti-Meziani and Mourad Oussalah). -- 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 Conclusions. -- Acknowledgements. -- Appendix 9A.1. -- References. -- 10 Mining Diagnostic Rules Using Fuzzy Clustering (Giovanna Castellano, Anna M. Fanelli and Corrado Mencar). -- 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 Concluding Remarks. -- References. -- 11 Fuzzy Regression Clustering (Mikal Sato-Ilic). -- 11.1 Introduction. -- 11.2 Statistical Weighted Regression Models. -- 11.3 Fuzzy Regression Clustering Models. -- 11.4 Analyses of Residuals on Fuzzy Regression Clustering Models. -- 11.5 Numerical Examples. -- 11.6 Conclusion. -- References. -- 12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling Using the Weighted Fuzzy C-means (George E. Tsekouras). -- 12.1 Introduction. -- 12.2 Takagi and Sugeno's Fuzzy Model. -- 12.3 Hierarchical Clustering-based Fuzzy Modeling. -- 12.4 Simulation Studies. -- 12.5 Conclusions. -- References. -- 13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data (Mario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni). -- 13.1 Introduction. -- 13.2 Dissimilarity Modeling. -- 13.3 Relational Clustering. -- 13.4 Experimental Results. -- 13.5 Conclusions. -- References. -- 14 Simultaneous Clustering and Feature Discrimination with Applications (Hichem Frigui). -- 14.1 Introduction. -- 14.2 Background. -- 14.3 Simultaneous Clustering and Attribute Discrimination (SCAD). -- 14.4 Clustering and Subset Feature Weighting. -- 14.5 Case of Unknown Number of Clusters. -- 14.6 Application 1: Color Image Segmentation. -- 14.7 Application 2: Text Document Categorization and Annotation. -- 14.8 Application 3: Building a Multi-modal Thesaurus from Annotated Images. -- 14.9 Conclusions. -- Appendix 14A.1. -- Acknowledgements. -- References. -- Part IV Real-time and Dynamic Clustering. -- 15 Fuzzy Clustering in Dynamic Data Mining - Techniques and Applications (Richard Weber). -- 15.1 Introduction. -- 15.2 Review of Literature Related to Dynamic Clustering. -- 15.3 Recent Approaches for Dynamic Fuzzy Clustering. -- 15.4 Applications. -- 15.5 Future Perspectives and Conclusions. -- Acknowledgement. -- References. -- 16 Fuzzy Clustering of Parallel Data Streams (Jürgen Beringer and Eyke Hüllermeier). -- 16.1 Introduction. -- 16.2 Background. -- 16.3 Preprocessing and Maintaining Data Streams. -- 16.4 Fuzzy Clustering of Data Streams. -- 16.5 Quality Measures. -- 16.6 Experimental Validation. -- 16.7 Conclusions. -- References. -- 17 Algorithms for Real-time Clustering and Generation of Rules from Data (Dimitar Filev and Plamer Angelov). -- 17.1 Introduction. -- 17.2 Density-based Real-time Clustering. -- 17.3 FSPC: Real-time Learning of Simplified Mamdani Models. -- 17.4 Applications. -- 17.5 Conclusion. -- References. -- Part V Applications and Case Studies. -- 18 Robust Exploratory Analysis of Magnetic Resonance Images using FCM with Feature Partitions (Mark D. Alexiuk and Nick J. Pizzi). -- 18.1 Introduction. -- 18.2 FCM with Feature Partitions. -- 18.3 Magnetic Resonance Imaging. -- 18.4 FMRI Analysis with FCMP. -- 18.5 Data-sets. -- 18.6 Results and Discussion. -- 18.7 Conclusion. -- Acknowledgements. -- References. -- 19 Concept Induction via Fuzzy C-means Clustering in a High-dimensional Semantic Space (Dawei Song, Guihong Cao, Peter Bruza and Raymond Lau). -- 19.1 Introduction. -- 19.2 Constructing a High-dimensional Semantic Space via Hyperspace Analogue to Language. -- 19.3 Fuzzy C-means Clustering. -- 19.4 Word Clustering on a HAL Space - A Case Study. -- 19.5 Conclusions and Future Work. -- Acknowledgement. -- References. -- 20 Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells using FTIR Spectroscopy (Xiao-Ying Wang, Jonathan M. Garibaldi, Benjamin Bird and Mike W. George). -- 20.1 Introduction. -- 20.2 Clustering Techniques. -- 20.3 Cluster Validity. -- 20.4 Simulated Annealing Fuzzy Clustering Algorithm. -- 20.5 Automatic Cluster Merging Method. -- 20.6 Conclusion. -- Acknowledgements. -- References. -- Index Notes Formerly CIP. Uk Bibliography Includes bibliographical references and index Notes Electronic version is available via Wiley InterScience Mode of access: Internet via World Wide Web Subject Fuzzy systems. Soft computing. Author Oliveira, J. Valente de (José Valente) Pedrycz, Witold, 1953- LC no. 2007278256 ISBN 0470027606 (hbk.) 9780470027608 (hbk.)

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