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

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 algorith...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Bin Gao, Tie-Yan Liu, Wei-Ying Ma
• Keywords: Data Mining, Heterogeneous Data, Heterogeneous Object Co-clustering, ICDM 2006, Web Image