Exemplar-based clustering has drawn much attention in recent years as it produces state-of-the-art results on many practical clustering problems. However, spatial information is missed in the exemplar-based clustering methods, resulting in difficulties in some applications, for example in the image segmentation problem. In this paper, we investigate the issue of integrating spatial information into the exemplar-based clustering through the Markov random field formulation. Two algorithms are proposed to achieve this aim. First, based on the min-sum loopy belief propagation algorithm, a spatially consistent affinity propagation algorithm is proposed. Second, by showing the spatially consistent exemplar-based clustering energy function satisfies the regular property, an efficient minimal s-t graph cut based convergent algorithm is proposed. Experimental results on the image segmentation problem show that the spatially consistent exemplar-based clustering achieves better results than other methods.