Clustering of Social Tagging System Users: A Topic and Time Based Approach
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
A fuzzy bi-clustering approach to correlate web users and pages
International Journal of Knowledge and Web Intelligence
Analyzing the behavioral structure characteristics from web traffic
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
In & out zooming on time-aware user/tag clusters
Journal of Intelligent Information Systems
A novel model for user clicks identification based on hidden semi-Markov
Journal of Network and Computer Applications
You are how you click: clickstream analysis for Sybil detection
SEC'13 Proceedings of the 22nd USENIX conference on Security
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Web users clustering is a crucial task for mining information related to users needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real datasets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time aware users clustering approaches. These clusters contain users exhibiting similar access behavior not only in terms of their page preferences but also of their access time.