Exemplar-based clustering methods partition the data space and identify the representative, or the exemplar, of each cluster. With the number of clusters adaptively determined, exemplar-based clustering methods are appealing since they avoid or alleviate the difficult task of estimating the latent parameters in case of complex models and high dimensionality of the data. Most exemplar-based clustering methods are based on generative models, where the exemplars serve as the parameters of the generative models. However, generative models do not consider the discriminative capability of the cluster boundaries explicitly described in discriminative models. In this paper, we present Discriminative Exemplar Clustering (DEC), that improves the discriminative power of exemplar-based clustering method by minimizing the misclassification error of the nonparametric unsupervised plug-in classifier while maintaining the appealing property of exemplar-based clustering. The optimization of DEC is performed in a pairwise Markov Random Field. Experimental results on synthetic and real data demonstrate the effectiveness of our method compared to other exemplar-based clustering methods.