Co-clustering methods have been widely applied to document clustering and gene expression analysis. These methods make use of the duality between features and samples such that the co-occurring structure of sample and feature clusters can be extracted. In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples. Most existing co-clustering methods conduct clustering on the graph achieved from the original data matrix, which doesn't have explicit cluster structure, thus they require a post-processing step to obtain the clustering results. In this paper, we propose a novel co-clustering method to learn a bipartite graph with exactly k connected components, where k is the number of clusters. The new bipartite graph learned in our model approximates the original graph but maintains an explicit cluster structure, from which we can immediately get the clustering results without post-processing. Extensive empirical results are presented to verify the effectiveness and robustness of our model.

Ingraph based co-clustering methods, abipartite graph isconstructed todepict the relation between features and samples.

In this work, wepresent G2SAT,thefirst deep generativeframework that learns to generate SAT formulas from a given set of input formulas.

Neural Information Processing Systems http://nips.cc/

Discovering clusters in bipartite graphs has been researched in many different settings. However, most of these algorithms were heuristics and do not provide theoretical guarantees for the quality oftheir results.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/