Co-clustering for Weblogs in Semantic Space

Yu Zong, Guandong Xu, Peter Dolog, Yanchun Zhang, Renjin Liu

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

1 Citation (Scopus)
338 Downloads (Pure)

Resumé

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.
OriginalsprogEngelsk
BogserieLecture Notes in Computer Science
Vol/bind6488
Sider (fra-til)120-127
ISSN0302-9743
DOI
StatusUdgivet - 12 dec. 2010
BegivenhedWeb Information Systems Engineering – WISE 2010 - Hong Kong, Kina
Varighed: 12 dec. 201014 dec. 2010

Konference

KonferenceWeb Information Systems Engineering – WISE 2010
LandKina
ByHong Kong
Periode12/12/201014/12/2010

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Semantics
Clustering
Latent Semantic Analysis
Clustering Algorithm
Clustering algorithms
Web Usage Mining
Probability Space
World Wide Web
Probability distributions
Probability Distribution
Websites
Partition
Metric
Experimental Results
Experiment
Object
Experiments

Citer dette

Zong, Yu ; Xu, Guandong ; Dolog, Peter ; Zhang, Yanchun ; Liu, Renjin. / Co-clustering for Weblogs in Semantic Space. I: Lecture Notes in Computer Science. 2010 ; Bind 6488. s. 120-127.
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Co-clustering for Weblogs in Semantic Space. / Zong, Yu; Xu, Guandong; Dolog, Peter; Zhang, Yanchun; Liu, Renjin.

I: Lecture Notes in Computer Science, Bind 6488, 12.12.2010, s. 120-127.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

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AB - 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.

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