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    On Mining Web Access Logs

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    45.pd.pdf (140.3Kb)
    Links to Files
    https://ebiquity.umbc.edu/paper/html/id/98/On-Mining-Web-Access-Logs
    Permanent Link
    http://hdl.handle.net/11603/12771
    Collections
    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    • UMBC Information Systems Department
    Metadata
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    Author/Creator
    Joshi, Anupam
    Joshi, Karuna
    Krishnapuram, Raghu
    Date
    1999-10-24
    Type of Work
    17 pages
    Text
    technical reports
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    world wide web
    user session
    access logs
    data mining
    UMBC Ebiquity Research Group
    Abstract
    The proliferation of information on the world wide web has made the personalization of this information space a necessity. One possible approach to web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classification or clustering methods seem to be ideally suited to analyze the semi-structured log data of user accesses. In this paper, we define the notion of a “user session”, as well as a dissimilarity measure between two web sessions that captures the organization of a web site. To extract a user access profile, we cluster the user sessions based on the pair-wise dissimilarities using a robust fuzzy clustering algorithm that we have developed. We report the results of experiments with our algorithm and show that this leads to extraction of interesting user profiles. We also show that it outperforms association rule based approaches for this task.


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.

     

     

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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.