Abstract
Several clustering algorithms include one or more parameters to be fixed before its application. This is also the case of fuzzy c-means, one of the most well-known fuzzy clustering algorithms, where two parameters c and m are required. c corresponds to the number of clusters and m to the fuzziness of the solutions. The selection of these parameters is a critical issue because a bad selection can blur the clusters in the data. In this paper we propose a method for selecting an appropriate parameter m for fuzzy c-means based on an extensive computation. Our approach is based on the application of the clustering algorithm to several instantiations of the same data with different degrees of noise.
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Jaimes, L.G., Torra, V. (2010). On the Selection of Parameter m in Fuzzy c-Means: A Computational Approach. In: Huynh, VN., Nakamori, Y., Lawry, J., Inuiguchi, M. (eds) Integrated Uncertainty Management and Applications. Advances in Intelligent and Soft Computing, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11960-6_41
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DOI: https://doi.org/10.1007/978-3-642-11960-6_41
Publisher Name: Springer, Berlin, Heidelberg
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