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Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno(T-S) fuzzy model identification. Inparticular, fuzzyc-regression clusteringbased on type-2 fuzzyset has been shown the remarkable results on non-sparsedatabut their performance degraded on sparse data. In this paper, aninnovativearchitecture for fuzzyc-regression model is presentedand a novelstudent-tdistribution based membership functionis designed for sparse datamodelling. To avoid the overfitting,we have adopted a Bayesian approach forincorporating aGaussian prior on the regression coefficients. Additionalnoveltyof our approach lies in type-reduction where the final output iscomputedusing Karnik Mendel algorithm and the consequentparameters of the model areoptimized using Stochastic GradientDescent method. As detailed experimentation,the result showsthat proposed approach outperforms on standard datasetsincomparison of various state-of-the-art methods.

A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification