Data clustering has found its usefulness in various fields. Algorithms are mostly developed using euclidean distance. But it has several drawbacks which maybe rectified by using kernel distance formula. In this paper, we propose a kernel based rough-fuzzy C-Means (KRFCM) algorithm and use modified version of the performance indexes (DB and D) obtained by replacing the distance function with kernel function. We provide a comparative analysis of RFCM with KRFCM by computing their DB and D index values. The analysis is based upon both numerical as well as image datasets. The results establish that the proposed algotihtm outperforms the existing one. © Springer-Verlag 2013.