In this paper, an optimal clustering technique for handwritten Nandinagari character recognition is proposed. We compare two different corner detector mechanisms and compare and contrast various clustering approaches for handwritten Nandinagari characters. In this model, the key interest points on the images which are invariant to Scale, rotation, translation, illumination and occlusion are identified by choosing robust Scale Invariant Feature Transform method(SIFT) and Speeded Up Robust Feature (SURF) transform techniques. We then generate a dissimilarity matrix, which is in turn fed as an input for a set of clustering techniques like K Means, PAM (Partition Around Medoids) and Hierarchical Agglomerative clustering. Various cluster validity measures are used to assess the quality of clustering techniques with an intent to find a technique suitable for these rare characters. On a varied data set of over 1040 Handwritten Nandinagari characters, a careful analysis indicate this combinatorial approach used in a collaborative manner will aid in achieving good recognition accuracy. We find that Hierarchical clustering technique is most suitable for SIFT and SURF features as compared to K Means and PAM techniques.