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2018 | OriginalPaper | Chapter

Gaussian Kernel-Based Fuzzy Clustering with Automatic Bandwidth Computation

Authors : Francisco de A. T. de Carvalho, Lucas V. C. Santana, Marcelo R. P. Ferreira

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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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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Metadata
Title
Gaussian Kernel-Based Fuzzy Clustering with Automatic Bandwidth Computation
Authors
Francisco de A. T. de Carvalho
Lucas V. C. Santana
Marcelo R. P. Ferreira
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_67

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