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Density based fuzzy c-means clustering of non-convex patterns
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.
History
Journal
European journal of operational researchVolume
173Issue
3Pagination
717 - 728Publisher
North-Holland Pub. CoLocation
Amsterdam, NetherlandsPublisher DOI
ISSN
0377-2217eISSN
1872-6860Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2005, Elsevier B.V.Usage metrics
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