A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications

The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of obje...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Viattchenin, Dmitri A. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Studies in Fuzziness and Soft Computing,
Temas:
Computational intelligence.
Data mining.
Artificial intelligence.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Artificial Intelligence.
Acceso en línea:Texto Completo

MARC

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245 1 2 |a A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications  |h [electronic resource] /  |c by Dmitri A. Viattchenin. 
250 |a 1st ed. 2013. 
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300 |a XII, 227 p.  |b online resource. 
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490 1 |a Studies in Fuzziness and Soft Computing,  |x 1860-0808 
505 0 |a Introduction -- Heuristic Algorithms of Possibilistic Clustering -- Clustering Approaches for the Uncertain Data -- Applications of the Heuristic Algorithms of Possibilistic Clustering. 
520 |a The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects.   The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover,  a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani's fuzzy inference systems is introduced. This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications. 
650 0 |a Computational intelligence. 
650 0 |a Data mining. 
650 0 |a Artificial intelligence. 
650 1 4 |a Computational Intelligence. 
650 2 4 |a Data Mining and Knowledge Discovery. 
650 2 4 |a Artificial Intelligence. 
710 2 |a SpringerLink (Online service) 
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950 |a Engineering (R0) (SpringerNature-43712) 

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