Abstract
Many clustering algorithms require some parameters that often are neither a priori known nor easy to estimate, like the number of classes. Measures of clustering quality can consequently be used to a posteriori estimate these values. This paper proposes such an index of clustering evaluation that deals with kernel methods like kernel-k-means. More precisely, it presents an extension of the well-known Davies & Bouldin’s index. Kernel clustering methods are particularly relevant because of their ability to deal with initially non-linearly separable clusters. The interest of the following clustering evaluation is then to get around the issue of the not explicitly known data transformation of such kernel methods. Kernel Davies & Bouldin’s index is finally used to a posteriori estimate the parameters of the kernel-k-means method applied on some toys datasets and Fisher’s Iris dataset.
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Nasser, A., Hébert, PA., Hamad, D. (2007). Clustering Evaluation in Feature Space. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_33
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DOI: https://doi.org/10.1007/978-3-540-74695-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
Online ISBN: 978-3-540-74695-9
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