Fuzzy clustering of non-convex patterns using global optimization
Contribuinte(s) |
Hong, Yan |
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Data(s) |
01/01/2001
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Resumo |
This paper discusses various extensions of the classical within-group sum of squared errors functional, routinely used as the clustering criterion. Fuzzy c-means algorithm is extended to the case when clusters have irregular shapes, by representing the clusters with more than one prototype. The resulting minimization problem is non-convex and non-smooth. A recently developed cutting angle method of global optimization is applied to this difficult problem <br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30004552/beliakov-fuzzyclusteringnon-convex-2001.pdf http://ieeexplore.ieee.org/iel5/7885/21726/01007287.pdf?tp= |
Direitos |
2001, IEEE |
Palavras-Chave | #fuzzy set theory #optimisation #pattern clustering |
Tipo |
Conference Paper |