UMBC ebiquity

On Mining Web Access Logs

Authors: Anupam Joshi, and Raghu Krishnapuram

Book Title: Proceedings of the SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

Date: May 14, 2000

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.

Type: InProceedings

Publisher: ACM

Pages: 63-69

Tags: web, web logs, datamining

Google Scholar: -iq2TlzQK-4J

Number of Google Scholar citations: 122 [show citations]

Number of downloads: 2286


Available for download as

size: 106321 bytes