A cluster is a collection of data objects which are similar to one another within the same cluster but dissimilar to objects in another cluster. This clustering mechanism ensures high intra-class similarity but low inter-class similarity which can be achieved by the cmeans architecture. Though much clustering algorithm has evolved since Hard CMeans, Fuzzy C Means (FCM) and Rough C Means (RCM) are widely used in applications for their superiority in handling vague and uncertain data. The probabilistic approach to clustering techniques can handle noisy data. In this paper we propose a hybrid clustering model on Possibilistic Rough C Means that can handle uncertainty even in presence of noisy data. The theory of Rough sets which has emerged as one of the most efficient tools of the decade can generate a pair of set with lower and upper approximations of the objects determined to establish the cluster. Possibilistic theory to cluster generates the typicality or possibility value of the objects that belongs to the rough cluster. This model will be more efficient and fool proof than each of the individual models. Experimental analysis reveals that the proposed algorithm enables to streamline the clustering process and have more precise clustering mechanisms.