Knowledge-Based Clustering: From Data to Information Granules / Edition 1

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This is must reading for researchers, professionals, and students interested in clustering, fuzzy clustering, unsupervised learning, neural networks, fuzzy sets, pattern recognition, and system modeling. With the author's emphasis on mastering the prerequisites, coupled with carefully constructed practical examples and experiments, readers will be well on their way to becoming knowledge-based clustering experts themselves.
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Editorial Reviews

From the Publisher
"I agree with Zadeh's opinion (mentioned at the end of book's foreword): 'The author and the publisher deserve our loud applause and congratulations.'" (Computing, May 19, 2005)
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Product Details

  • ISBN-13: 9780471469667
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 1/14/2005
  • Edition number: 1
  • Pages: 336
  • Product dimensions: 6.34 (w) x 9.59 (h) x 0.83 (d)

Meet the Author

WITOLD PEDRYCZ, PHD, is a Professor and Canada Research Chair at the University of Alberta, Canada. He is also with the Systems Research Institute of The Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a Fellow of the IEEE, has authored nine research monographs, edited six volumes, and has written numerous papers in computational intelligence, granular computing, pattern recognition, quantitative software engineering, and data mining.
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Table of Contents

1 Clustering and fuzzy clustering 1
2 Computing with granular information : fuzzy sets and fuzzy relations 28
3 Logic-oriented neurocomputing 50
4 Conditional fuzzy clustering 66
5 Clustering with partial supervision 87
6 Principles of knowledge-based guidance in fuzzy clustering 97
7 Collaborative clustering 129
8 Directional clustering 158
9 Fuzzy relational clustering 178
10 Fuzzy clustering of heterogeneous patterns 191
11 Hyperbox models of granular data : the Tchebyschev FCM 209
12 Genetic tolerance fuzzy neural networks 226
13 Granular prototyping 246
14 Granular mappings 270
15 Linguistic modeling 283
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