Bayesian Fuzzy Clustering

Abstract:

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.

 

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Plain Text:

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

BibTex:

@Article{Glenn2015Bayesian,
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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