A novel Kernel Method for clustering

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.