Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/578
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dc.contributor.authorPetridou, Sophia-
dc.contributor.authorKoutsonikola, Vassiliki A.-
dc.contributor.authorVakali, Athena I.-
dc.contributor.authorPapadimitriou, Georgios I.-
dc.date.accessioned2020-01-09T10:59:14Z-
dc.date.available2020-01-09T10:59:14Z-
dc.date.issued2008-05-
dc.identifier10.1109/TKDE.2007.190741en_US
dc.identifier.issn1041-4347en_US
dc.identifier.urihttps://doi.org/10.1109/TKDE.2007.190741en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/578-
dc.description.abstractWeb 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.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.sourceIEEE Transactions on Knowledge and Data Engineeringen_US
dc.subjectFRASCATI::Natural sciences::Computer and information sciencesen_US
dc.subject.otherWeb miningen_US
dc.subject.otherWeb users clusteringen_US
dc.subject.otherNavigationen_US
dc.subject.otherAccess timeen_US
dc.titleTime-Aware Web Users' Clusteringen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume20en_US
local.identifier.issue5en_US
local.identifier.firstpage653en_US
local.identifier.lastpage667en_US
local.identifier.eissn1558-2191en_US
Appears in Collections:Department of Applied Informatics

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