Details

Advances in Fuzzy Clustering and its Applications


Advances in Fuzzy Clustering and its Applications


1. Aufl.

von: Jose Valente de Oliveira, Witold Pedrycz

110,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 13.06.2007
ISBN/EAN: 9780470061183
Sprache: englisch
Anzahl Seiten: 454

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Beschreibungen

<b>A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering</b>. <p>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, the book covers a wealth of novel, original and fully updated material, and in particular offers:</p> <ul> <li>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.</li> <li>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</li> <li>demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects</li> <li>a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role</li> </ul> <p>This 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.</p>
<p>List of Contributors xi</p> <p>Foreword xv</p> <p>Preface xvii</p> <p><b>Part I Fundamentals 1</b></p> <p><b>1 Fundamentals of Fuzzy Clustering 3</b><br /><i>Rudolf Kruse, Christian Döring and Marie-Jeanne Lesot</i></p> <p>1.1 Introduction 3</p> <p>1.2 Basic Clustering Algorithms 4</p> <p>1.3 Distance Function Variants 14</p> <p>1.4 Objective Function Variants 18</p> <p>1.5 Update Equation Variants: Alternating Cluster Estimation 25</p> <p>1.6 Concluding Remarks 27</p> <p>Acknowledgements 28</p> <p>References 29</p> <p><b>2 Relational Fuzzy Clustering 31</b><br /><i>Thomas A. Runkler</i></p> <p>2.1 Introduction 31</p> <p>2.2 Object and Relational Data 31</p> <p>2.3 Object Data Clustering Models 34</p> <p>2.4 Relational Clustering 38</p> <p>2.5 Relational Clustering with Non-spherical Prototypes 41</p> <p>2.6 Relational Data Interpreted as Object Data 45</p> <p>2.7 Summary 46</p> <p>2.8 Experiments 46</p> <p>2.9 Conclusions 49</p> <p>References 50</p> <p><b>3 Fuzzy Clustering with Minkowski Distance Functions 53</b><br /><i>Patrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen</i></p> <p>3.1 Introduction 53</p> <p>3.2 Formalization 54</p> <p>3.3 The Majorizing Algorithm for Fuzzy C-means with Minkowski Distances 56</p> <p>3.4 The Effects of the Robustness Parameter l 60</p> <p>3.5 Internet Attitudes 62</p> <p>3.6 Conclusions 65</p> <p>References 66</p> <p><b>4 Soft Cluster Ensembles 69</b><br /><i>Kunal Punera and Joydeep Ghosh</i></p> <p>4.1 Introduction 69</p> <p>4.2 Cluster Ensembles 71</p> <p>4.3 Soft Cluster Ensembles 75</p> <p>4.4 Experimental Setup 78</p> <p>4.5 Soft vs. Hard Cluster Ensembles 82</p> <p>4.6 Conclusions and Future Work 90</p> <p>Acknowledgements 90</p> <p>References 90</p> <p><b>Part II Visualization 93</b></p> <p><b>5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures 95</b><br /><i>János Abonyi and Balázs Feil</i></p> <p>5.1 Problem Definition 97</p> <p>5.2 Classical Methods for Cluster Validity and Merging 99</p> <p>5.3 Similarity of Fuzzy Clusters 100</p> <p>5.4 Visualization of Clustering Results 103</p> <p>5.5 Conclusions 116</p> <p>Appendix 5A.1 Validity Indices 117</p> <p>Appendix 5A.2 The Modified Sammon Mapping Algorithm 120</p> <p>Acknowledgements 120</p> <p>References 120</p> <p><b>6 Interactive Exploration of Fuzzy Clusters 123</b><br /><i>Bernd Wiswedel, David E. Patterson and Michael R. Berthold</i></p> <p>6.1 Introduction 123</p> <p>6.2 Neighborgram Clustering 125</p> <p>6.3 Interactive Exploration 131</p> <p>6.4 Parallel Universes 135</p> <p>6.5 Discussion 136</p> <p>References 136</p> <p><b>Part III Algorithms and Computational Aspects 137</b></p> <p><b>7 Fuzzy Clustering with Participatory Learning and Applications 139</b><br /><i>Leila Roling Scariot da Silva, Fernando Gomide and Ronald Yager</i></p> <p>7.1 Introduction 139</p> <p>7.2 Participatory Learning 140</p> <p>7.3 Participatory Learning in Fuzzy Clustering 142</p> <p>7.4 Experimental Results 145</p> <p>7.5 Applications 148</p> <p>7.6 Conclusions 152</p> <p>Acknowledgements 152</p> <p>References 152</p> <p><b>8 Fuzzy Clustering of Fuzzy Data 155</b><br /><i>Pierpaolo D’Urso</i></p> <p>8.1 Introduction 155</p> <p>8.2 Informational Paradigm, Fuzziness and Complexity in Clustering Processes 156</p> <p>8.3 Fuzzy Data 160</p> <p>8.4 Fuzzy Clustering of Fuzzy Data 165</p> <p>8.5 An Extension: Fuzzy Clustering Models for Fuzzy Data Time Arrays 176</p> <p>8.6 Applicative Examples 180</p> <p>8.7 Concluding Remarks and Future Perspectives 187</p> <p>References 189</p> <p><b>9 Inclusion-based Fuzzy Clustering 193</b><br /><i>Samia Nefti-Meziani and Mourad Oussalah</i></p> <p>9.1 Introduction 193</p> <p>9.2 Background: Fuzzy Clustering 195</p> <p>9.3 Construction of an Inclusion Index 196</p> <p>9.4 Inclusion-based Fuzzy Clustering 198</p> <p>9.5 Numerical Examples and Illustrations 201</p> <p>9.6 Conclusions 206</p> <p>Acknowledgements 206</p> <p>Appendix 9A.1 207</p> <p>References 208</p> <p><b>10 Mining Diagnostic Rules Using Fuzzy Clustering 211</b><br /><i>Giovanna Castellano, Anna M. Fanelli and Corrado Mencar</i></p> <p>10.1 Introduction 211</p> <p>10.2 Fuzzy Medical Diagnosis 212</p> <p>10.3 Interpretability in Fuzzy Medical Diagnosis 213</p> <p>10.4 A Framework for Mining Interpretable Diagnostic Rules 216</p> <p>10.5 An Illustrative Example 221</p> <p>10.6 Concluding Remarks 226</p> <p>References 226</p> <p><b>11 Fuzzy Regression Clustering 229</b><br /><i>Mikal Sato-Ilic</i></p> <p>11.1 Introduction 229</p> <p>11.2 Statistical Weighted Regression Models 230</p> <p>11.3 Fuzzy Regression Clustering Models 232</p> <p>11.4 Analyses of Residuals on Fuzzy Regression Clustering Models 237</p> <p>11.5 Numerical Examples 242</p> <p>11.6 Conclusion 245</p> <p>References 245</p> <p><b>12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling Using the Weighted Fuzzy C-means 247</b><br /><i>George E. Tsekouras</i></p> <p>12.1 Introduction 247</p> <p>12.2 Takagi and Sugeno’s Fuzzy Model 248</p> <p>12.3 Hierarchical Clustering-based Fuzzy Modeling 249</p> <p>12.4 Simulation Studies 256</p> <p>12.5 Conclusions 261</p> <p>References 261</p> <p><b>13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data 265</b><br /><i>Mario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni</i></p> <p>13.1 Introduction 265</p> <p>13.2 Dissimilarity Modeling 267</p> <p>13.3 Relational Clustering 275</p> <p>13.4 Experimental Results 280</p> <p>13.5 Conclusions 281</p> <p>References 281</p> <p><b>14 Simultaneous Clustering and Feature Discrimination with Applications 285</b><br /><i>Hichem Frigui</i></p> <p>14.1 Introduction 285</p> <p>14.2 Background 287</p> <p>14.3 Simultaneous Clustering and Attribute Discrimination (SCAD) 289</p> <p>14.4 Clustering and Subset Feature Weighting 296</p> <p>14.5 Case of Unknown Number of Clusters 298</p> <p>14.6 Application 1: Color Image Segmentation 298</p> <p>14.7 Application 2: Text Document Categorization and Annotation 302</p> <p>14.8 Application 3: Building a Multi-modal Thesaurus from Annotated Images 305</p> <p>14.9 Conclusions 309</p> <p>Appendix 14A.1 310</p> <p>Acknowledgements 311</p> <p>References 311</p> <p><b>Part IV Real-time and Dynamic Clustering 313</b></p> <p><b>15 Fuzzy Clustering in Dynamic Data Mining – Techniques and Applications 315</b><br /><i>Richard Weber</i></p> <p>15.1 Introduction 315</p> <p>15.2 Review of Literature Related to Dynamic Clustering 315</p> <p>15.3 Recent Approaches for Dynamic Fuzzy Clustering 317</p> <p>15.4 Applications 324</p> <p>15.5 Future Perspectives and Conclusions 331</p> <p>Acknowledgement 331</p> <p>References 331</p> <p><b>16 Fuzzy Clustering of Parallel Data Streams 333</b><br /><i>Jürgen Beringer and Eyke Hüllermeier</i></p> <p>16.1 Introduction 333</p> <p>16.2 Background 334</p> <p>16.3 Preprocessing and Maintaining Data Streams 336</p> <p>16.4 Fuzzy Clustering of Data Streams 340</p> <p>16.5 Quality Measures 343</p> <p>16.6 Experimental Validation 345</p> <p>16.7 Conclusions 350</p> <p>References 351</p> <p><b>17 Algorithms for Real-time Clustering and Generation of Rules from Data 353</b><br /><i>Dimitar Filev and Plamer Angelov</i></p> <p>17.1 Introduction 353</p> <p>17.2 Density-based Real-time Clustering 355</p> <p>17.3 FSPC: Real-time Learning of Simplified Mamdani Models 358</p> <p>17.4 Applications 362</p> <p>17.5 Conclusion 367</p> <p>References 368</p> <p><b>Part V Applications and Case Studies 371</b></p> <p><b>18 Robust Exploratory Analysis of Magnetic Resonance Images using FCM with Feature Partitions 373</b><br /><i>Mark D. Alexiuk and Nick J. Pizzi</i></p> <p>18.1 Introduction 373</p> <p>18.2 FCM with Feature Partitions 374</p> <p>18.3 Magnetic Resonance Imaging 379</p> <p>18.4 FMRI Analysis with FCMP 381</p> <p>18.5 Data-sets 382</p> <p>18.6 Results and Discussion 384</p> <p>18.7 Conclusion 390</p> <p>Acknowledgements 390</p> <p>References 390</p> <p><b>19 Concept Induction via Fuzzy C-means Clustering in a High-dimensional Semantic Space 393</b><br /><i>Dawei Song, Guihong Cao, Peter Bruza and Raymond Lau</i></p> <p>19.1 Introduction 393</p> <p>19.2 Constructing a High-dimensional Semantic Space via Hyperspace Analogue to Language 395</p> <p>19.3 Fuzzy C-means Clustering 397</p> <p>19.4 Word Clustering on a HAL Space – A Case Study 399</p> <p>19.5 Conclusions and Future Work 402</p> <p>Acknowledgement 402</p> <p>References 402</p> <p><b>20 Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells using FTIR Spectroscopy 405</b><br /><i>Xiao-Ying Wang, Jonathan M. Garibaldi, Benjamin Bird and Mike W. George</i></p> <p>20.1 Introduction 405</p> <p>20.2 Clustering Techniques 406</p> <p>20.3 Cluster Validity 412</p> <p>20.4 Simulated Annealing Fuzzy Clustering Algorithm 413</p> <p>20.5 Automatic Cluster Merging Method 418</p> <p>20.6 Conclusion 423</p> <p>Acknowledgements 424</p> <p>References 424</p> <p>Index 427</p>
Researchers, as well as those with incipient interest in the field, will find this book very useful and informative. (<i>Computing Reviews</i>, July 8, 2008)
<b>José Valente de Oliveira</b> 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. <p><b>Witold Pedrycz</b> 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.</p>
<b>A comprehensive, coherent, and in depth presentation of the state of the art in fuzzy clustering</b>. <p>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, the book covers a wealth of novel, original and fully updated material, and in particular offers:</p> <ul> <li> 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.</li> <li> 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</li> <li> demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects</li> <li> a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role</li> </ul> <p>This 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.</p>

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