HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
Proceedings of the the seventh ACM conference on Hypertext
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Automatic personalization based on Web usage mining
Communications of the ACM
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
A Unified Framework for Clustering Heterogeneous Web Objects
WISE '02 Proceedings of the 3rd International Conference on Web Information Systems Engineering
Utilizing hyperlink transitivity to improve web page clustering
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Correlation-based Document Clustering using Web Logs
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 5 - Volume 5
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Discovering user access pattern based on probabilistic latent factor model
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Using Probabilistic Latent Semantic Analysis for Web Page Grouping
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Web clustering is an approach to aggregate web objects into various categories according to underlying relationships among them. Finding co-clusters of web objects is an emerging topic in the context of web usage mining. In this paper we will present an algorithm using tensor decomposition to co-cluster web objects based on analysis of user navigational tasks. The usage data of users visiting web sites is collected as experimental data to construct the usage network and validate the presented method. Experimental results have demonstrated the proposed method can clearly reveal the aggregations of web objects as a result of different navigational tasks.