On Mining Web Access Logs

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https://ebiquity.umbc.edu/paper/html/id/98/On-Mining-Web-Access-Logs

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http://hdl.handle.net/11603/12771

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UMBC Information Systems Department
UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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Date

1999-10-24

Type of Work

technical reports
Text

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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.