Title :
Bayesian Fuzzy Clustering
Author :
Glenn, Taylor C. ; Zare, Alina ; Gader, Paul D.
Author_Institution :
Dept. of Comput. & Inf. Sci. & Eng., Univ. of Florida, Gainesville, FL, USA
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.
Keywords :
fuzzy set theory; inference mechanisms; parameter estimation; pattern clustering; probability; Bayesian fuzzy clustering model; Bayesian probabilistic model; fuzzy c-means approach; inference algorithm; model parameter estimation; particle filter inference technique; Bayes methods; Clustering algorithms; Data models; Mathematical model; Probabilistic logic; Proposals; Prototypes; Bayes methods; clustering algorithms; clustering methods; fuzzy sets; fuzzy systems; monte carlo methods; particle filters;
Journal_Title :
Fuzzy Systems, IEEE Transactions on
DOI :
10.1109/TFUZZ.2014.2370676
