Advances in Fuzzy Clustering and its Applications
1. Auflage April 2007
454 Seiten, Hardcover
Wiley & Sons Ltd
Kurzbeschreibung
Divided into four sections, Advances in Fuzzy Clustering and its Applications first explores the essentials of fuzzy clustering, including motivation, basic algorithms, computing aspects, realizations, cluster validity assessment, and ensuing interpretation of the results along with several representative areas of applications. Chapters presenting the underlying fundamentals i.e. the "Foundations"of fuzzy clustering follow. The third section "Algorithms and Computational Aspects" focuses on the algorithmic and computational augmentations of fuzzy clustering and demonstrates its effectiveness in highly dimensional problems, distributed problem solving and uncertainty handling. The final section, "Applications and Case Studies," explores a series of applications in which fuzzy clustering plays a pivotal role.
A 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, the book covers a wealth of novel, original and fully updated material, and in particular offers:
* 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.
* 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
* demonstrations of how the results facilitate further detailed development of models, and enhance interpretation aspects
* a carefully organized illustrative series of applications and case studies in which fuzzy clustering plays a pivotal role
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.
Foreword.
Preface.
Part I Fundamentals.
1 Fundamentals of Fuzzy Clustering (Rudolf Kruse, Christian Döring and Marie-Jeanne Lesot).
2 Relational Fuzzy Clustering (Thomas A. Runkler).
3 Fuzzy Clustering with Minkowski Distance Functions (Patrick J.F. Groenen, Uzay Kaymak and Joost van Rosmalen).
4 Soft Cluster Ensembles (Kunal Punera and Joydeep Ghosh).
Part II Visualization.
5 Aggregation and Visualization of Fuzzy Clusters Based on Fuzzy Similarity Measures (János Abonyi and Balázs Feil).
6 Interactive Exploration of Fuzzy Clusters (Bernd Wiswedel, David E. Patterson and Michael R. Berthold).
Part III Algorithms and Computational Aspects.
7 Fuzzy Clustering with Participatory Learning and Applications (Leila Roling Scariot da Silva, Fernando Gomide and Ronald Yager).
8 Fuzzy Clustering of Fuzzy Data (Pierpaolo D'Urso).
9 Inclusion-based Fuzzy Clustering (Samia Nefti-Meziani and Mourad Oussalah).
10 Mining Diagnostic Rules Using Fuzzy Clustering (Giovanna Castellano, Anna M. Fanelli and Corrado Mencar).
11 Fuzzy Regression Clustering (Mikal Sato-Ilic).
12 Implementing Hierarchical Fuzzy Clustering in Fuzzy Modeling Using the Weighted Fuzzy C-means (George E. Tsekouras).
13 Fuzzy Clustering Based on Dissimilarity Relations Extracted from Data (Mario G.C.A. Cimino, Beatrice Lazzerini and Francesco Marcelloni).
14 Simultaneous Clustering and Feature Discrimination with Applications (Hichem Frigui).
Part IV Real-time and Dynamic Clustering.
15 Fuzzy Clustering in Dynamic Data Mining - Techniques and Applications (Richard Weber).
16 Fuzzy Clustering of Parallel Data Streams (Jürgen Beringer and Eyke Hüllermeier).
17 Algorithms for Real-time Clustering and Generation of Rules from Data (Dimitar Filev and Plamer Angelov).
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).
19 Concept Induction via Fuzzy C-means Clustering in a High-dimensional Semantic Space (Dawei Song, Guihong Cao, Peter Bruza and Raymond Lau).
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).
Index.
Witold Pedrycz 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.