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A new approach to session identification by applying fuzzy c-means clustering on web logs

Ερευνητική Περιοχή: Clustering Έτος: 2016
Είδος Δημοσίευσης: Σε Συνεδρίο Λέξεις Κλειδιά: Browsers;Cleaning;Clustering algorithms;Euclidean distance;Partitioning algorithms;Web servers
Τίτλος Βιβλίου: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Σελίδες: 1-8
Μήνας: Δεκέμβριος
ISBN: 9781509042401
In this paper a new algorithm for session identification in web logs is outlined, based on the fuzzy c-means clustering of the available data. The novelty of the proposed methodology lies in the initialization of the partition matrix using subtractive clustering, the examination of the effect a variety of distance metrics have on the clustering process (in addition to the widely-used Euclidean distance), the determination of the number of user sessions based on candidate sessions and the representation of the session data. The experimental results show that the proposed methodology is effective in the reconstruction of user sessions and can distinguish individual sessions more accurately than baseline time-heuristic methods proposed in literature.

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