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.
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Camastra, F., Verri, A. (2005). A Novel Kernel Method for Clustering. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_29
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DOI: https://doi.org/10.1007/1-4020-3432-6_29
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
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