Co-clustering for weblogs in semantic space

Yu ZONG, Guandong XU, Peter DOLOG, Yanchun ZHANG, Renjin LIU

Research output: Chapter in Book/Report/Conference proceedingChapters

2 Citations (Scopus)


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. Copyright © 2010 Springer-Verlag Berlin Heidelberg.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering, WISE 2010: 11th International Conference, Proceedings
EditorsLei CHEN, Peter TRIANTAFILLOU, Torsten SUEL
ISBN (Electronic)9783642176166
ISBN (Print)9783642176159
Publication statusPublished - 2010


Zong, Y., Xu, G., Dolog, P., Zhang, Y. & Liu, R. (2010). Co-clustering for weblogs in semantic space. In L. Chen, P. Triantafillou, & T. Suel (Eds,). Web Information Systems Engineering, WISE 2010: 11th International Conference, Proceedings (pp. 120-127). Springer.


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