Co-clustering for weblogs in semantic space

  • Authors:

  • Yu Zong;Guandong Xu;Peter Dolog;Yanchun Zhang;Renjin Liu

  • Affiliations:

  • Department of Information and Engineering, West Anhui University, China;Intelligent Web and Information Systems, Aalborg University, Computer Science Department, Aalborg, Denmark and Center for Applied Informatics, School of Engineering & Science, Victoria Univers ...;Intelligent Web and Information Systems, Aalborg University, Computer Science Department, Aalborg, Denmark;Center for Applied Informatics, School of Engineering & Science, Victoria University, Vic, Australia;Department of Information and Engineering, West Anhui University, China

  • Venue:
  • WISE'10 Proceedings of the 11th international conference on Web information systems engineering
  • Year:
  • 2010

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Abstract

Web clustering is an approach for aggregating web objects into various groups according to underlying relationships among them. Finding co-clusters of web objects in semantic space is an interesting topic in the context of web usage mining, which is able to capture the underlying user navigational interest and content preference simultaneously. In this paper we will present a novel web co-clustering algorithm named Co-Clustering in Semantic space (COCS) to simultaneously partition web users and pages via a latent semantic analysis approach. In COCS, we first, train the latent semantic space of weblog data by using Probabilistic Latent Semantic Analysis (PLSA) model, and then, project all weblog data objects into this semantic space with probability distribution to capture the relationship among web pages and web users, at last, propose a clustering algorithm to generate the co-cluster corresponding to each semantic factor in the latent semantic space via probability inference. The proposed approach is evaluated by experiments performed on real datasets in terms of precision and recall metrics. Experimental results have demonstrated the proposed method can effectively reveal the co-aggregates of web users and pages which are closely related.