In this paper we introduce the concept of rough intuitionistic fuzzy k-mode algorithm to cluster categorical data. This proposal is an extension of rough fuzzy k-mode in which we have added the parameter intuitionistic degree in the calculation of membership values of all elements in a given cluster. The efficiency of the proposed algorithm is demonstrated using various popular categorical data sets from UCI data repository. Experimental analysis is performed by taking these data sets and several measures of efficiency like the DB index, D index, XB index, PC pair and Minkowski score are computed for each data set. The results invariably show that the proposed algorithm is more efficient than rough fuzzy k-mode algorithm.