Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 27, Issue 5, Pages 801-U4Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2005.88
Keywords
kernel methods; one class SVM; clustering algorithms; EM algorithm; K-means
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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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