  Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno
(T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering
based on type-2 fuzzyset has been shown the remarkable results on non-sparse
databut their performance degraded on sparse data. In this paper, aninnovative
architecture for fuzzyc-regression model is presentedand a novel
student-tdistribution based membership functionis designed for sparse data
modelling. To avoid the overfitting,we have adopted a Bayesian approach for
incorporating aGaussian prior on the regression coefficients. Additional
noveltyof our approach lies in type-reduction where the final output iscomputed
using Karnik Mendel algorithm and the consequentparameters of the model are
optimized using Stochastic GradientDescent method. As detailed experimentation,
the result showsthat proposed approach outperforms on standard datasets
incomparison of various state-of-the-art methods.
