| Creators: |
Faußer, Stefan A. and Schwenker, Friedhelm |
| Title: |
Clustering large datasets with kernel methods |
| Item Type: |
Conference or Workshop Item |
| Event Title: |
(Proceedings of the) 21st International Conference on Pattern Recognition. (ICPR ’12) ; Vol. 1 |
| Event Location: |
Tsukuba, Japan |
| Event Dates: |
November, 11-15th, 2012 |
| Page Range: |
pp. 501-504 |
| Date: |
2012 |
| Divisions: |
Informationsmanagement |
| Abstract (ENG): |
Real-life datasets are becoming larger and less linear separable. Divisive clustering methods with a computation time linear to the number of samples n can handle large data but mostly assume linear boundaries between the cluster in input space. Kernel based clustering methods are able to detect nonlinear boundaries in feature space but have a quadratic computation time O(n2). In this paper, we propose a meta-algorithm that distributes small-sized subset of the large dataset, parallelized cluster these subset and merges the resulting approximate pseudo-centre repeatedly until the whole dataset has been processed. The meta-algorithm is able to use a wide range of kernel based clustering methods. Here we integrate Kernel Fuzzy C-Means and Relational Neural Gas. We analytically show that the algorithm has a linear computation time O(n). In the experiments we empirically evaluate the performance of the method on two real-life datasets. |
| Forthcoming: |
No |
| Language: |
English |
| Citation: |
Faußer, Stefan A. and Schwenker, Friedhelm
(2012)
Clustering large datasets with kernel methods.
In: (Proceedings of the) 21st International Conference on Pattern Recognition. (ICPR ’12) ; Vol. 1, November, 11-15th, 2012, Tsukuba, Japan, pp. 501-504.
ISBN 9781467322164
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