Abstract
The conventional Gaussian kernel-based fuzzy c-means clustering algorithm has widely demonstrated its superiority to the conventional fuzzy c-means when the data sets are arbitrarily shaped, and not linearly separable. However, its performance is very dependent on the estimation of the bandwidth parameter of the Gaussian kernel function. Usually this parameter is estimated once and for all. This paper presents a Gaussian fuzzy c-means with kernelization of the metric which depends on a vector of bandwidth 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., Santana, L.V.C., Ferreira, M.R.P. (2018). Gaussian Kernel-Based Fuzzy Clustering with Automatic Bandwidth Computation. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_67
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