document: Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications . While the co-clustering algorithms for two types of heterogeneous data ( denoted by pair-wise co-clustering ) , such as documents and terms , have been well studied in the literature , the work on more types of heterogeneous data ( denoted by high-order co-clustering ) is still very limited . As an attempt in this direction , in this paper , we worked on a specific case of high-order co-clustering in which there is a central type of objects that connects the other types so as to form a star structure of the inter-relationships . Actually , this case could be a very good abstract for many real-world applications , such as the co-clustering of categories , documents and terms in text mining . In our philosophy , we treated such kind of problems as the fusion of multiple pair-wise co-clustering sub-problems with the constraint of the star structure . Accordingly , we proposed the concept of consistent bipartite graph co-partitioning , and developed an algorithm based on semi-definite programming ( SDP ) for efficient computation of the clustering results . Experiments on toy problems and real data both verified the effectiveness of our proposed method .
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