Abstract
The fuzzy c-means proposed by Dunn and Bezdek is one of the most popular methods of fuzzy clustering. Clusters obtained by the fuzzy c-means are in the Voronoi sets when crisp reallocation rule is applied. This means that a part of a larger cluster may be assigned to a smaller one when there are clusters of different sizes. Therefore, some methods using variables for controlling cluster sizes have been proposed. In this paper, we study their theoretical properties and compare them using numerical examples.
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Komazaki, Y., Miyamoto, S. (2013). Variables for Controlling Cluster Sizes on Fuzzy c-Means. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_17
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DOI: https://doi.org/10.1007/978-3-642-41550-0_17
Publisher Name: Springer, Berlin, Heidelberg
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