ReCoM: reinforcement clustering of multi-type interrelated data objects

Authors:
Jidong Wang;Huajun Zeng;Zheng Chen;Hongjun Lu;Li Tao;Wei-Ying Ma
Affiliations:
Microsoft Research Asia, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China;Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;Microsoft Research Asia, Beijing, P.R.China;Microsoft Research Asia, Beijing, P.R.China
Venue:
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Year:
2003

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Abstract

Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.