Co-clustering for Weblogs in Semantic Space

Springer Berlin / Heidelberg
Publication Type:
Conference Proceeding
Web Information Systems Engineering WISE 2010 Lecture Notes in Computer Science, 2010, 6488 pp. 120 - 127
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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 simultane- ously. In this paper we will present a novel web co-clustering algorithm named Co-Clustering in Semantic space (COCS) to simultaneously par- tition 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 distribu- tion 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 in- ference. 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.
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