PROJECT TITLE :

Bayesian Fuzzy Clustering

ABSTRACT:

We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that gives expanded capabilities over the traditional Fuzzy C-Means approach. Additionally, we tend to 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 together with the amount of clusters. We show results on artificial and real knowledge and compare with other approaches.


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