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 with other approaches.

Links:

Citation:

T. C. Glenn, A. Zare, and P. Gader, “Bayesian fuzzy clustering,” IEEE Trans. Fuzzy Syst., vol. 23, iss. 5, pp. 1545-1561, 2015. 

@Article{glenn2015bayesian,
Title = {Bayesian fuzzy clustering},
Author = {Glenn, Taylor C. and Zare, Alina and Gader, Paul},
Journal = {IEEE Trans. Fuzzy Syst.},
Year = {2015},
Month = {Oct.},
Number = {5},
Pages = {1545-1561},
Volume = {23},
Doi = {10.1109/TFUZZ.2014.2370676},
}