Density based fuzzy c-means clustering of non-convex patterns
Data(s) |
16/09/2006
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Resumo |
We propose a new technique to perform unsupervised data classification (clustering) based on density induced metric and non-smooth optimization. Our goal is to automatically recognize multidimensional clusters of non-convex shape. We present a modification of the fuzzy c-means algorithm, which uses the data induced metric, defined with the help of Delaunay triangulation. We detail computation of the distances in such a metric using graph algorithms. To find optimal positions of cluster prototypes we employ the discrete gradient method of non-smooth optimization. The new clustering method is capable to identify non-convex overlapped d-dimensional clusters.<br /><br /><br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
North-Holland Pub. Co |
Relação |
http://dro.deakin.edu.au/eserv/DU:30003583/beliakov-clu_ejor-2006.pdf http://dro.deakin.edu.au/eserv/DU:30003583/beliakov-cluenjor-2006.pdf http://dx.doi.org/10.1016/j.ejor.2005.10.007 |
Direitos |
2005, Elsevier B.V. |
Palavras-Chave | #data mining #non-linear programming #clustering #fuzzy c-means #non-smooth optimization |
Tipo |
Journal Article |