Co-clustering exploits co-occurrence information, from contingency tables to cluster both rows and columns simultaneously. It has been established that co-clustering produces a better clustering structure as compared to conventional methods of clustering. So far, co-clustering has only been used as a technique for producing hard clusters, which might be inadequate for applications such as document clustering. In this paper, we present an algorithm using the information theoretic approach  to generate overlapping (soft) clusters. The algorithm maintains probability membership for every instance to each of the possible clusters and iteratively tunes these membership values. The theoretical formulation of the criterion function is presented first, followed by the actual algorithm. We evaluate the algorithm over document/word co-occurrence information and present experimental results.