Abstract
Fuzzy \(c\)-means is one of the most well known fuzzy clustering algorithms. It is usually solved using an iterative algorithm. This algorithm does not ensure that the solution is the global optimum. In this paper we study the distribution of values of the objective function of fuzzy \(c\)-means.
We also propose a new fuzzy clustering method related to fuzzy \(c\)-means. The method presumes that the shape of the membership function is known and can be calculated from the cluster centers, which are the only results of the clustering algorihm.
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Torra, V. (2015). On Fuzzy \(c\)-Means and Membership Based Clustering. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_49
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DOI: https://doi.org/10.1007/978-3-319-19258-1_49
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