Web clustering is an approach for aggregating Web objects into various groups according to underlying relationships among them. Finding co-clusters of Web objects 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 an algorithm using bipartite spectral clustering to co-cluster Web users and pages. The usage data of users visiting Web sites is modeled as a bipartite graph and the spectral clustering is then applied to the graph representation of usage data. The proposed approach is evaluated by experiments performed on real datasets, and the impact of using various clustering algorithms is also investigated. Experimental results have demonstrated the employed method can effectively reveal the subset aggregates of Web users and pages which are closely related. Copyright © 2010 Springer-Verlag Berlin Heidelberg.
|Title of host publication
|Knowledge-based and intelligent information and engineering systems: 14th International Conference, KES 2010, Cardiff, UK, september 8-10, 2010, proceedings, part III
|Rossitza SETCHI, Ivan JORDANOV, Robert J. HOWLETT, Lakhmi C. JAIN
|Place of Publication
|Published - 2010