Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
482646 | European Journal of Operational Research | 2006 | 12 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Gleb Beliakov, Matthew King,