{"product_id":"advances-in-fuzzy-clustering-and-its-applications-isbn-9780470027608","title":"Advances in Fuzzy Clustering and its Applications","description":"\u003cb\u003eA comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering\u003c\/b\u003e.  \u003cp\u003eFuzzy 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, the book covers a wealth of novel, original and fully updated material, and in particular offers:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ea focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management.\u003c\/li\u003e \u003cli\u003epresentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling\u003c\/li\u003e \u003cli\u003edemonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects\u003c\/li\u003e \u003cli\u003ea carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.\u003c\/p\u003e \u003cp\u003eList of Contributors xi\u003c\/p\u003e \u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Fundamentals 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Fundamentals of Fuzzy Clustering 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRudolf Kruse, Christian Döring and Marie-Jeanne Lesot\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Basic Clustering Algorithms 4\u003c\/p\u003e \u003cp\u003e1.3 Distance Function Variants 14\u003c\/p\u003e \u003cp\u003e1.4 Objective Function Variants 18\u003c\/p\u003e \u003cp\u003e1.5 Update Equation Variants: Alternating Cluster Estimation 25\u003c\/p\u003e \u003cp\u003e1.6 Concluding Remarks 27\u003c\/p\u003e \u003cp\u003eAcknowledgements 28\u003c\/p\u003e \u003cp\u003eReferences 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Relational Fuzzy Clustering 31\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThomas A. Runkler\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 31\u003c\/p\u003e \u003cp\u003e2.2 Object and Relational Data 31\u003c\/p\u003e \u003cp\u003e2.3 Object Data Clustering Models 34\u003c\/p\u003e \u003cp\u003e2.4 Relational Clustering 38\u003c\/p\u003e \u003cp\u003e2.5 Relational Clustering with Non-spherical Prototypes 41\u003c\/p\u003e \u003cp\u003e2.6 Relational Data Interpreted as Object Data 45\u003c\/p\u003e \u003cp\u003e2.7 Summary 46\u003c\/p\u003e \u003cp\u003e2.8 Experiments 46\u003c\/p\u003e \u003cp\u003e2.9 Conclusions 49\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fuzzy Clustering with Minkowski Distance Functions 53\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePatrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 53\u003c\/p\u003e \u003cp\u003e3.2 Formalization 54\u003c\/p\u003e \u003cp\u003e3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances 56\u003c\/p\u003e \u003cp\u003e3.4 The Effects of the Robustness Parameter l 60\u003c\/p\u003e \u003cp\u003e3.5 Internet Attitudes 62\u003c\/p\u003e \u003cp\u003e3.6 Conclusions 65\u003c\/p\u003e \u003cp\u003eReferences 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Soft Cluster Ensembles 69\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKunal Punera and Joydeep Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 69\u003c\/p\u003e \u003cp\u003e4.2 Cluster Ensembles 71\u003c\/p\u003e \u003cp\u003e4.3 Soft Cluster Ensembles 75\u003c\/p\u003e \u003cp\u003e4.4 Experimental Setup 78\u003c\/p\u003e \u003cp\u003e4.5 Soft vs. Hard Cluster Ensembles 82\u003c\/p\u003e \u003cp\u003e4.6 Conclusions and Future Work 90\u003c\/p\u003e \u003cp\u003eAcknowledgements 90\u003c\/p\u003e \u003cp\u003eReferences 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Visualization 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures 95\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJános Abonyi and Balázs Feil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Problem Definition 97\u003c\/p\u003e \u003cp\u003e5.2 Classical Methods for Cluster Validity and Merging 99\u003c\/p\u003e \u003cp\u003e5.3 Similarity of Fuzzy Clusters 100\u003c\/p\u003e \u003cp\u003e5.4 Visualization of Clustering Results 103\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 116\u003c\/p\u003e \u003cp\u003eAppendix 5A.1 Validity Indices 117\u003c\/p\u003e \u003cp\u003eAppendix 5A.2 The Modified Sammon Mapping Algorithm 120\u003c\/p\u003e \u003cp\u003eAcknowledgements 120\u003c\/p\u003e \u003cp\u003eReferences 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Interactive Exploration of Fuzzy Clusters 123\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBernd Wiswedel, David E. Patterson and Michael R. Berthold\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 123\u003c\/p\u003e \u003cp\u003e6.2 Neighborgram Clustering 125\u003c\/p\u003e \u003cp\u003e6.3 Interactive Exploration 131\u003c\/p\u003e \u003cp\u003e6.4 Parallel Universes 135\u003c\/p\u003e \u003cp\u003e6.5 Discussion 136\u003c\/p\u003e \u003cp\u003eReferences 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Algorithms and Computational Aspects 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Fuzzy Clustering with Participatory Learning and Applications 139\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLeila Roling Scariot da Silva, Fernando Gomide and Ronald Yager\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 139\u003c\/p\u003e \u003cp\u003e7.2 Participatory Learning 140\u003c\/p\u003e \u003cp\u003e7.3 Participatory Learning in Fuzzy Clustering 142\u003c\/p\u003e \u003cp\u003e7.4 Experimental Results 145\u003c\/p\u003e \u003cp\u003e7.5 Applications 148\u003c\/p\u003e \u003cp\u003e7.6 Conclusions 152\u003c\/p\u003e \u003cp\u003eAcknowledgements 152\u003c\/p\u003e \u003cp\u003eReferences 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fuzzy Clustering of Fuzzy Data 155\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePierpaolo D’Urso\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 155\u003c\/p\u003e \u003cp\u003e8.2 Informational Paradigm, Fuzziness and Complexity in Clustering Processes 156\u003c\/p\u003e \u003cp\u003e8.3 Fuzzy Data 160\u003c\/p\u003e \u003cp\u003e8.4 Fuzzy Clustering of Fuzzy Data 165\u003c\/p\u003e \u003cp\u003e8.5 An Extension: Fuzzy Clustering Models for Fuzzy Data Time Arrays 176\u003c\/p\u003e \u003cp\u003e8.6 Applicative Examples 180\u003c\/p\u003e \u003cp\u003e8.7 Concluding Remarks and Future Perspectives 187\u003c\/p\u003e \u003cp\u003eReferences 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Inclusion-based Fuzzy Clustering 193\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSamia Nefti-Meziani and Mourad Oussalah\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 193\u003c\/p\u003e \u003cp\u003e9.2 Background: Fuzzy Clustering 195\u003c\/p\u003e \u003cp\u003e9.3 Construction of an Inclusion Index 196\u003c\/p\u003e \u003cp\u003e9.4 Inclusion-based Fuzzy Clustering 198\u003c\/p\u003e \u003cp\u003e9.5 Numerical Examples and Illustrations 201\u003c\/p\u003e \u003cp\u003e9.6 Conclusions 206\u003c\/p\u003e \u003cp\u003eAcknowledgements 206\u003c\/p\u003e \u003cp\u003eAppendix 9A.1 207\u003c\/p\u003e \u003cp\u003eReferences 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Mining Diagnostic Rules Using Fuzzy Clustering 211\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGiovanna Castellano, Anna M. Fanelli and Corrado Mencar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 211\u003c\/p\u003e \u003cp\u003e10.2 Fuzzy Medical Diagnosis 212\u003c\/p\u003e \u003cp\u003e10.3 Interpretability in Fuzzy Medical Diagnosis 213\u003c\/p\u003e \u003cp\u003e10.4 A Framework for Mining Interpretable Diagnostic Rules 216\u003c\/p\u003e \u003cp\u003e10.5 An Illustrative Example 221\u003c\/p\u003e \u003cp\u003e10.6 Concluding Remarks 226\u003c\/p\u003e \u003cp\u003eReferences 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Fuzzy Regression Clustering 229\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMikal Sato-Ilic\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 229\u003c\/p\u003e \u003cp\u003e11.2 Statistical Weighted Regression Models 230\u003c\/p\u003e \u003cp\u003e11.3 Fuzzy Regression Clustering Models 232\u003c\/p\u003e \u003cp\u003e11.4 Analyses of Residuals on Fuzzy Regression Clustering Models 237\u003c\/p\u003e \u003cp\u003e11.5 Numerical Examples 242\u003c\/p\u003e \u003cp\u003e11.6 Conclusion 245\u003c\/p\u003e \u003cp\u003eReferences 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling Using the Weighted Fuzzy C-means 247\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGeorge E. Tsekouras\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 247\u003c\/p\u003e \u003cp\u003e12.2 Takagi and Sugeno’s Fuzzy Model 248\u003c\/p\u003e \u003cp\u003e12.3 Hierarchical Clustering-based Fuzzy Modeling 249\u003c\/p\u003e \u003cp\u003e12.4 Simulation Studies 256\u003c\/p\u003e \u003cp\u003e12.5 Conclusions 261\u003c\/p\u003e \u003cp\u003eReferences 261\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data 265\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 265\u003c\/p\u003e \u003cp\u003e13.2 Dissimilarity Modeling 267\u003c\/p\u003e \u003cp\u003e13.3 Relational Clustering 275\u003c\/p\u003e \u003cp\u003e13.4 Experimental Results 280\u003c\/p\u003e \u003cp\u003e13.5 Conclusions 281\u003c\/p\u003e \u003cp\u003eReferences 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Simultaneous Clustering and Feature Discrimination with Applications 285\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHichem Frigui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 285\u003c\/p\u003e \u003cp\u003e14.2 Background 287\u003c\/p\u003e \u003cp\u003e14.3 Simultaneous Clustering and Attribute Discrimination (SCAD) 289\u003c\/p\u003e \u003cp\u003e14.4 Clustering and Subset Feature Weighting 296\u003c\/p\u003e \u003cp\u003e14.5 Case of Unknown Number of Clusters 298\u003c\/p\u003e \u003cp\u003e14.6 Application 1: Color Image Segmentation 298\u003c\/p\u003e \u003cp\u003e14.7 Application 2: Text Document Categorization and Annotation 302\u003c\/p\u003e \u003cp\u003e14.8 Application 3: Building a Multi-modal Thesaurus from Annotated Images 305\u003c\/p\u003e \u003cp\u003e14.9 Conclusions 309\u003c\/p\u003e \u003cp\u003eAppendix 14A.1 310\u003c\/p\u003e \u003cp\u003eAcknowledgements 311\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Real-time and Dynamic Clustering 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Fuzzy Clustering in Dynamic Data Mining – Techniques and Applications 315\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRichard Weber\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 315\u003c\/p\u003e \u003cp\u003e15.2 Review of Literature Related to Dynamic Clustering 315\u003c\/p\u003e \u003cp\u003e15.3 Recent Approaches for Dynamic Fuzzy Clustering 317\u003c\/p\u003e \u003cp\u003e15.4 Applications 324\u003c\/p\u003e \u003cp\u003e15.5 Future Perspectives and Conclusions 331\u003c\/p\u003e \u003cp\u003eAcknowledgement 331\u003c\/p\u003e \u003cp\u003eReferences 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Fuzzy Clustering of Parallel Data Streams 333\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJürgen Beringer and Eyke Hüllermeier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 333\u003c\/p\u003e \u003cp\u003e16.2 Background 334\u003c\/p\u003e \u003cp\u003e16.3 Preprocessing and Maintaining Data Streams 336\u003c\/p\u003e \u003cp\u003e16.4 Fuzzy Clustering of Data Streams 340\u003c\/p\u003e \u003cp\u003e16.5 Quality Measures 343\u003c\/p\u003e \u003cp\u003e16.6 Experimental Validation 345\u003c\/p\u003e \u003cp\u003e16.7 Conclusions 350\u003c\/p\u003e \u003cp\u003eReferences 351\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Algorithms for Real-time Clustering and Generation of Rules from Data 353\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDimitar Filev and Plamer Angelov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 353\u003c\/p\u003e \u003cp\u003e17.2 Density-based Real-time Clustering 355\u003c\/p\u003e \u003cp\u003e17.3 FSPC: Real-time Learning of Simplified Mamdani Models 358\u003c\/p\u003e \u003cp\u003e17.4 Applications 362\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 367\u003c\/p\u003e \u003cp\u003eReferences 368\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Applications and Case Studies 371\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Robust Exploratory Analysis of Magnetic Resonance Images using FCM with Feature Partitions 373\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMark D. Alexiuk and Nick J. Pizzi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 373\u003c\/p\u003e \u003cp\u003e18.2 FCM with Feature Partitions 374\u003c\/p\u003e \u003cp\u003e18.3 Magnetic Resonance Imaging 379\u003c\/p\u003e \u003cp\u003e18.4 FMRI Analysis with FCMP 381\u003c\/p\u003e \u003cp\u003e18.5 Data-sets 382\u003c\/p\u003e \u003cp\u003e18.6 Results and Discussion 384\u003c\/p\u003e \u003cp\u003e18.7 Conclusion 390\u003c\/p\u003e \u003cp\u003eAcknowledgements 390\u003c\/p\u003e \u003cp\u003eReferences 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Concept Induction via Fuzzy C-means Clustering in a High-dimensional Semantic Space 393\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDawei Song, Guihong Cao, Peter Bruza and Raymond Lau\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 393\u003c\/p\u003e \u003cp\u003e19.2 Constructing a High-dimensional Semantic Space via Hyperspace Analogue to Language 395\u003c\/p\u003e \u003cp\u003e19.3 Fuzzy C-means Clustering 397\u003c\/p\u003e \u003cp\u003e19.4 Word Clustering on a HAL Space – A Case Study 399\u003c\/p\u003e \u003cp\u003e19.5 Conclusions and Future Work 402\u003c\/p\u003e \u003cp\u003eAcknowledgement 402\u003c\/p\u003e \u003cp\u003eReferences 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells using FTIR Spectroscopy 405\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eXiao-Ying Wang, Jonathan M. Garibaldi, Benjamin Bird and Mike W. George\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 405\u003c\/p\u003e \u003cp\u003e20.2 Clustering Techniques 406\u003c\/p\u003e \u003cp\u003e20.3 Cluster Validity 412\u003c\/p\u003e \u003cp\u003e20.4 Simulated Annealing Fuzzy Clustering Algorithm 413\u003c\/p\u003e \u003cp\u003e20.5 Automatic Cluster Merging Method 418\u003c\/p\u003e \u003cp\u003e20.6 Conclusion 423\u003c\/p\u003e \u003cp\u003eAcknowledgements 424\u003c\/p\u003e \u003cp\u003eReferences 424\u003c\/p\u003e \u003cp\u003eIndex 427\u003c\/p\u003e Researchers, as well as those with incipient interest in the field, will find this book very useful and informative. (\u003ci\u003eComputing Reviews\u003c\/i\u003e, July 8, 2008)  \u003cb\u003eJosé Valente de Oliveira\u003c\/b\u003e received his Ph.D. (1996), M.Sc. (1992), and the “Licenciado” degree in Electrical and Computer Engineering from the IST, Technical University of Lisbon.  Currently he is an Assistant Professor in the Faculty of Science and Technology at the University of Algarve where he served as Deputy Dean from 2002-2003.  He was recently appointed director of the University of Algarve Informatics Lab, a research laboratory specializing in computational intelligence including fuzzy sets, fuzzy and intelligent systems, machine learning, and optimization.  \u003cp\u003e\u003cb\u003eWitold Pedrycz\u003c\/b\u003e is a Professor and Canada Research Chair (CRC) in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada.  He is also with the Systems Research Institute of the Polish Academy of Sciences.  He is actively pursuing research in computational intelligence, fuzzy modeling, knowledge discovery and data mining, fuzzy control including fuzzy controllers, pattern recognition, knowledge-based neural networks, relational computation, bioinformatics, and Software Engineering.  He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systems.\u003c\/p\u003e  \u003cb\u003eA comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering\u003c\/b\u003e.  \u003cp\u003eFuzzy 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, the book covers a wealth of novel, original and fully updated material, and in particular offers:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e a focus on the algorithmic and computational augmentations of fuzzy clustering and its effectiveness in handling high dimensional problems, distributed problem solving and uncertainty management.\u003c\/li\u003e \u003cli\u003e presentations of the important and relevant phases of cluster design, including the role of information granules, fuzzy sets in the realization of human-centricity facet of data analysis, as well as system modelling\u003c\/li\u003e \u003cli\u003e demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects\u003c\/li\u003e \u003cli\u003e a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis book will be of key interest to engineers associated with fuzzy control, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering disciplines, will find this an invaluable resource and research tool.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988681539813,"sku":"NP9780470027608","price":144.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470027608.jpg?v=1761781225","url":"https:\/\/k12savings.com\/es\/products\/advances-in-fuzzy-clustering-and-its-applications-isbn-9780470027608","provider":"K12savings","version":"1.0","type":"link"}