The claimed advantage of describing a document data set with a bipartite graph is that partitioning such a graph yields a co-clustering of words and documents. The topic of each cluster can then be represented by the top words and documents that have highest within- cluster degrees. However, such claims may fail if top words and documents are selected simply because they are very general and frequent. In addition, for those words and documents across several topics, it may not be proper to assign them to a single cluster. To that end, this paper introduces a new bipartite formulation that incorporates both word hypercliques and document hypercliques as super vertices. By copreserving hypercliqiie patterns during the clustering process, our experiments on real-world data sets show that better clustering results can be obtained and the cluster topic can be more precisely identified. Also, we illustrate an application of the partitioned bipartite to search engines, returning clustered search results for keyword queries. We show that the topic of each cluster with respect to the current query can be identified more accurately with the words and documents from the patterns than with those top ones from the standard bipartite formulation.