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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Camastra, F. (2006). Kernel Methods for Clustering. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_1
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DOI: https://doi.org/10.1007/11731177_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
Online ISBN: 978-3-540-33184-1
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