Robust Clustering methods are aimed at avoiding unsatisfactory results resulting from the presence of certainamount of outlying observations in the input data of many practical applications such as biological sequences analysis or gene expressions analysis. This paper presents an algorithm termed as rough possibilistic complete link clustering algorithm (RPLINK) that maximizes the average similarity between pairs of patterns within the same cluster and at the same time the size of a cluster is maximized by computing the zeros of the derivative of appropriate objective function. The proposed algorithm is comprised of a judicious integration of the principles of rough sets, Complete Link and possibilistic clustering paradigm. This integration enables robust selection of the minimum set of the most informative bio-bases from the lower bound of the produced clusters. RPLINK along with the proposed initialization procedure with a few less sensitive input parameters shows a high outliers rejection capability as it makes their membership very low furthermore it does not requires the number of clusters to be known in advance and it can discover clusters of non convex shape. The effectiveness and robustness of the proposed algorithms, along with a comparison with other algorithms, have been demonstrated on different types of protein data sets.