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Star-Structured High-Order Heterogeneous Data Co-clustering based on Consistent Information Theory

Bin Gao, Tie-Yan Liu, and Wei-Ying Ma

Abstract

Heterogeneous object co-clustering has become an important research topic in data mining. In early years of this research, people mainly worked on two types of heterogeneous data (denoted by pair-wise co-clustering); while recently more and more attention was paid to multiple types of heterogeneous data (denoted by highorder co-clustering). In this paper, we studied the highorder co-clustering of objects with star-structured interrelationship, i.e., there is a central type of objects that connects the other types of objects. Actually, this case could be a very good model for many real-world applications, such as the co-clustering of Web images, their low-level visual features, and the surrounding text. We used a tripartite graph to represent the interrelationships among different objects, and proposed a consistent information theory which generates an effective algorithm to obtain the co-clusters of different types of objects. Experiments on a Web image show that our proposed algorithm is a better choice compared with previous work on heterogeneous object co-clustering.

Details

Publication typeArticle
Published inProceedings of the Sixth International Conference on Data Mining
PublisherIEEE Computer Society
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