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Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based on the low-density separation assumption, which unifies the merits of both Bayesian and discriminative approaches. In addition to stating prior distribution on functions explicitly as traditional Gaussian processes, special prior knowledge can be embedded into BMMC implicitly via the Universum set easily. Furthermore, it is much easier to solve a BMMC than an MMC since the integer variables in the optimization are eliminated. Experimental results show that the BMMC achieves comparable or even better performance than state-of-the-art clustering methods and solving BMMC is more efficiently.