_ A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics _ Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible _ Includes illustrative material andwell-known experimentsto offer hands-on experience
_ A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics _ Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible _ Includes illustrative material andwell-known experimentsto offer hands-on experience
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.
Inhaltsangabe
Foreword. Preface. 1. Clustering and Fuzzy Clustering. 2. Computing with Granular Information: Fuzzy Sets and Fuzzy Relations. 3. Logic-Oriented Neurocomputing. 4. Conditional Fuzzy Clustering. 5. Clustering with Partial Supervision. 6. Principles of Knowledge-Based Guidance in Fuzzy Clustering. 7. Collaborative Clustering. 8. Directional Clustering. 9. Fuzzy Relational Clustering. 10. Fuzzy Clustering of Heterogeneous Patterns. 11. Hyperbox Models of Granular Data: The Tchebyschev FCM. 12. Genetic Tolerance Fuzzy Neural Networks. 13. Granular Prototyping. 14. Granular Mappings. 15. Linguistic Modeling. Bibliography. Index.
Foreword. Preface. 1. Clustering and Fuzzy Clustering. 2. Computing with Granular Information: Fuzzy Sets and Fuzzy Relations. 3. Logic-Oriented Neurocomputing. 4. Conditional Fuzzy Clustering. 5. Clustering with Partial Supervision. 6. Principles of Knowledge-Based Guidance in Fuzzy Clustering. 7. Collaborative Clustering. 8. Directional Clustering. 9. Fuzzy Relational Clustering. 10. Fuzzy Clustering of Heterogeneous Patterns. 11. Hyperbox Models of Granular Data: The Tchebyschev FCM. 12. Genetic Tolerance Fuzzy Neural Networks. 13. Granular Prototyping. 14. Granular Mappings. 15. Linguistic Modeling. Bibliography. Index.
Rezensionen
"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 Reviews.com , May 19, 2005)
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