Time-Aware Web Users' Clustering

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 acc...

• Type: Journal
• Year: 2008
• Where: TKDE
• Authors: Sophia G. Petridou, Vassiliki A. Koutsonikola, Athena Vakali, Georgios I. Papadimitriou
• Keywords: Page Preferences, Popular Clustering Approaches, TKDE 2008, Web Users