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EnglishKernel Methods are algorithms that implicitly perform a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we present a novel Kernel Method, Kernel K-Means for clustering problems. Unlike other popular clustering algorithms that yield piecewise linear borders among data, Kernel K-Means allows to get nonlinear separation surfaces in the data. Kernel K-Means compares better with popular clustering algorithms, on a synthetic dataset and two UCI real data benchmarks.
| Titolo: | A Novel Kernel Method for Clustering |
| Autori: | |
| Data di pubblicazione: | 2006 |
| Abstract: | Kernel Methods are algorithms that implicitly perform a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we present a novel Kernel Method, Kernel K-Means for clustering problems. Unlike other popular clustering algorithms that yield piecewise linear borders among data, Kernel K-Means allows to get nonlinear separation surfaces in the data. Kernel K-Means compares better with popular clustering algorithms, on a synthetic dataset and two UCI real data benchmarks. |
| Handle: | http://hdl.handle.net/11367/25928 |
| ISBN: | 978-1402034312 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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