Idioma:

A possibilistic approach to clustering

Krishnapuram, R. ; Keller, J.M.

IEEE transactions on fuzzy systems, 1993-05, Vol.1 (2), p.98-110 [Periódico revisado por pares]

Texto completo disponível

Citações Citado por

Título:
A possibilistic approach to clustering
Autor: Krishnapuram, R. ; Keller, J.M.
Assuntos: Clustering algorithms ; Clustering methods ; Computer vision ; Cybernetics ; Equations ; Face detection ; Iterative algorithms ; Partitioning algorithms ; Pattern recognition ; Possibility theory ; Prototypes
É parte de: IEEE transactions on fuzzy systems, 1993-05, Vol.1 (2), p.98-110
Notas: CDMS
Legacy CDMS
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: The clustering problem is cast in the framework of possibility theory. The approach differs from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values can be interpreted as degrees of possibility of the points belonging to the classes, i.e., the compatibilities of the points with the class prototypes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function. The advantages of the resulting family of possibilistic algorithms are illustrated by several examples.< >
Editor: Legacy CDMS: IEEE
Idioma: Inglês

Voltar para lista de resultados

Anterior Resultado 8 Avançar Ir para próxima página