Knowledge-Based Clustering

From Data to Information Granules

Witold Pedrycz

316 pages, parution le 15/03/2005

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Résumé

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.

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

Sommaire

Clustering and Fuzzy Clustering
Computing with Granular Information: Fuzzy Sets and Fuzzy Relations
Logic-Oriented Neurocomputing
Conditional Fuzzy Clustering
Clustering with Partial Supervision
Principles of Knowledge-Based Guidance in Fuzzy Clustering
Collaborative Clustering
Directional Clustering
Fuzzy Relational Clustering
Fuzzy Clustering of Heterogeneous Patterns
Hyperbox Models of Granular Data: The Tchebyschev FCM
Genetic Tolerance Fuzzy Neural Networks
Granular Prototyping
Granular Mappings
Linguistic Modeling

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Witold Pedrycz
Parution	15/03/2005
Nb. de pages	316
Format	16 x 24
Couverture	Relié
Poids	590g
Intérieur	Noir et Blanc
EAN13	9780471469667
ISBN13	978-0-471-46966-7