Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach

Xu, G; Zong, Y; Dolog, P; Zhang, Y

Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach

Xu, G

Zong, Y Dolog, P Zhang, Y

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Publisher:: Springer Berlin / Heidelberg
Publication Type:: Conference Proceeding
Citation:: Knowledge-Based and Intelligent Information and Engineering Systems Lecture Notes in Computer Science, 2010, 4781/2007 pp. 398 - 407
Issue Date:: 2010-01

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Field	Value	Language
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Zong, Y	en_US
dc.contributor.author	Dolog, P	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.editor	NA	en_US
dc.date	2014-09-08	en_US
dc.date.issued	2010-01	en_US
dc.identifier.citation	Knowledge-Based and Intelligent Information and Engineering Systems Lecture Notes in Computer Science, 2010, 4781/2007 pp. 398 - 407	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29147
dc.identifier.uri	http://hdl.handle.net/10453/33791
dc.description.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 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 cocluster 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.	en_US
dc.publisher	Springer Berlin / Heidelberg	en_US
dc.relation.ispartof	Knowledge-Based and Intelligent Information and Engineering Systems Lecture Notes in Computer Science	en_US
dc.relation.ispartof	International Conference on Knowledge-Based and Intelligent Information and Engineering Systems	en_US
dc.relation.isbasedon	10.1007/978-3-642-15393-8_45	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Co-clustering Analysis of Weblogs Using Bipartite Spectral Projection Approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4781/2007	en_US
utslib.location	Germany	en_US
utslib.location.activity	Cardiff, UK	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Cardiff, UK	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Germany	en_US
pubs.start-date	2014-09-08	en_US
pubs.volume	4781/2007	en_US

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 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 cocluster 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.

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http://hdl.handle.net/10453/29147