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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a new pairwise clustering framework where nonparametric pairwise similarity is derived by minimizing the generalization error unsupervised nonparametric classifier. The proposed framework bridges the gap between clustering and multi-class classification, and explains the widely used kernel similarity for clustering. The authors also prove that the generalization error bound for the unsupervised plug-in classifier is asymptotically equal to the weighted volume of cluster boundary for low density separation. Based on the derived nonparametric pairwise similarity using the plug-in classifier, the authors propose a new nonparametric exemplar-based clustering method with enhanced discriminative capability compared to the exiting exemplar-based clustering methods.