A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory which minimizes the resulting loss in mutual information. A divisive clustering algorithm that monotonically reduces this loss function was recently proposed. In this paper we show that sparse high-dimensional data presents special challenges which can result in the algorithm getting stuck at poor local minima. We propose two solutions to this problem: (a) a "prior" to overcome infinite relative entropy values as in the supervised Naive Bayes algorithm, and (b) local search to escape local minima. Finally, we combine these solutions to get a robust algorithm that is computationally efficient. We present experimental results to show that the proposed method is effective in clustering document collections and outperforms previous information-theoretic clustering approaches.