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	Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering
	Gao, Bin ; Liu, Tie-Yan ; Zheng, Xin ; Cheng, Qian-Sheng ; Ma, Wei-Ying
	2005
英文摘要	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 interrelationships. 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. Copyright 2005 ACM.; EI; 0
语种	英语
DOI标识	10.1145/1081870.1081879
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/315553]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Gao, Bin,Liu, Tie-Yan,Zheng, Xin,et al. Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering. 2005-01-01.

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