Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/578
Title: Time-Aware Web Users' Clustering
Authors: Petridou, Sophia G.
Koutsonikola, Vassiliki A.
Vakali, Athena I.
Papadimitriou, Georgios I.
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Web mining
Web users clustering
Navigation
Access time
Issue Date: May-2008
Publisher: IEEE
Source: IEEE Transactions on Knowledge and Data Engineering
Volume: 20
Issue: 5
First Page: 653
Last Page: 667
Abstract: 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 data sets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time-aware user clustering approaches. These clusters contain users exhibiting similar access behavior in terms not only of their page preferences but also of their access time.
URI: https://doi.org/10.1109/TKDE.2007.190741
https://ruomo.lib.uom.gr/handle/7000/578
ISSN: 1041-4347
Electronic ISSN: 1558-2191
Other Identifiers: 10.1109/TKDE.2007.190741
Appears in Collections:Department of Applied Informatics

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