Based on the maximum a posteriori (MAP) principle and Bayesian framework, the Bayesian fuzzy clustering (BFC) method recently proposed exhibits promising characteristics in estimating the number of clusters and finding the globally optimal clustering solution, for the method effectively combines the advantages of both probability theory and fuzzy theory. However, since it suffers from its high computational burden, BFC becomes impractical for large-scale datasets. In this paper, in order to circumvent this drawback of BFC, a weighted Bayesian fuzzy clustering (WBFC) algorithm is first proposed by introducing weighting mechanism in BFC. Then, a fast single pass Bayesian fuzzy clustering (SPBFC) algorithm is developed by combining WBFC with a single pass clustering framework. Theoretical analysis on convergence and time complexity is also discussed. The experimental results show that SPBFC not only inherits the promising characteristics, but also has a fast convergence speed for large-scale datasets.