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A novel kernel method for clustering

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2 Author(s)
Camastra, F. ; INFM-DISI, Genova Univ., Italy ; Verri, A.

Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, we present a kernel method for clustering inspired by the classical k-means algorithm in which each cluster is iteratively refined using a one-class support vector machine. Our method, which can be easily implemented, compares favorably with respect to popular clustering algorithms, like k-means, neural gas, and self-organizing maps, on a synthetic data set and three UCI real data benchmarks (IRIS data, Wisconsin breast cancer database, Spam database).

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:27 ,  Issue: 5 )