Abstract
The clustering performance of the conventional gaussian kernel based clustering algorithms are very dependent on the estimation of the width hyper-parameter of the gaussian kernel function. Usually this parameter is estimated once and for all. This paper presents a gaussian c-Means with kernelization of the metric which depends on a vector of width hyper-parameters, one for each variable, that are computed automatically. Experiments with data sets of the UCI machine learning repository corroborate the usefulness of the proposed algorithm.
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Acknowledgments
The authors are grateful to the anonymous referees for their careful revision, and CNPq and FACEPE (Brazilian agencies) for their financial support.
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de Carvalho, F.d.A.T., Ferreira, M.R.P., Simões, E.C. (2016). A Gaussian Kernel-based Clustering Algorithm with Automatic Hyper-parameters Computation. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks – ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. Springer, Cham. https://doi.org/10.1007/978-3-319-40663-3_45
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DOI: https://doi.org/10.1007/978-3-319-40663-3_45
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