Bayesian Fuzzy Clustering


We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that provides expanded capabilities over the traditional Fuzzy C-Means approach. Additionally we extend the Bayesian Fuzzy Clustering model to handle a variable number of clusters and present a particle filter inference technique to estimate the model parameters including the number of clusters. We show results on synthetic and real data and compare to other approaches.




IEEE Abstract



Plain Text:

Glenn, T., Zare, A., Gader, P., “Bayesian Fuzzy Clustering,” IEEE Trans. Fuzzy Syst., vol. 23, no. 5, pp. 1545-1561, Oct. 2015.


Title = {Bayesian Fuzzy Clustering},
Author = {Glenn, Taylor and Zare, Alina and Gader, Paul},
Journal = {IEEE Trans. Fuzzy Syst.},
Year = {2015},
Month = {Oct.},
Number = {5},
Pages = {1545-1561},
Volume = {23}

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