Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering. The algorithm compares better with popular clustering algorithms, namely K-Means, Neural Gas, Self Organizing Maps, on a synthetic dataset and three UCI real data benchmarks, IRIS data, Wisconsin breast cancer database, Spam database.

Kernel Methods for Clustering

CAMASTRA, Francesco
2006

Abstract

Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering. The algorithm compares better with popular clustering algorithms, namely K-Means, Neural Gas, Self Organizing Maps, on a synthetic dataset and three UCI real data benchmarks, IRIS data, Wisconsin breast cancer database, Spam database.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/26257
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