Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering

Authors:
Bin Gao;Tie-Yan Liu;Xin Zheng;Qian-Sheng Cheng;Wei-Ying Ma
Affiliations:
Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China;Tsinghua University, Beijing, P. R. China;Peking University, Beijing, P. R. China;Microsoft Research Asia, Beijing, P. R. China
Venue:
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Year:
2005

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Abstract

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.