Automatic personalization based on Web usage mining
Communications of the ACM
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
The use of web structure and content to identify subjectively interesting web usage patterns
ACM Transactions on Internet Technology (TOIT)
A Robust Clustering Algorithm Based on Competitive Agglomeration and Soft Rejection of Outliers
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Simulating the effectiveness of using association rules for recommendation systems
AsiaSim'04 Proceedings of the Third Asian simulation conference on Systems Modeling and Simulation: theory and applications
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Each user accesses a Web site with certain interests. The interest can be manifested by the sequence of each Web user access. The access paths of all Web users can be clustered. The effectiveness and efficiency are two problems in clustering algorithms. This paper provides a clustering algorithm for personalized Web recommendation. It is path clustering based on competitive agglomeration (PCCA). The path similarity and the center of a cluster are defined for the proposed algorithm. The algorithm relies on competitive agglomeration to get best cluster numbers automatically. Recommending based on the algorithm doesn't disturb users and needn't any registration information. Experiments are performed to compare the proposed algorithm with two other algorithms and the results show that the improvement of recommending performance is significant.