Using data mining techniques on Web access logs to dynamically improve hypertext structure

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Abstract
With the growing popularity of the World Wide Web (Web), large volumes of data such as user address or URL requested are gathered automatically by Web servers and collected in access log files. Discovering relationships and global patterns that exist in such files can provide significant and useful information for performance enhancement, restructuring a Web site for increased effectiveness, and customer targeting in electronic commerce. In this paper, we propose an integrated system (WebTool) for applying data mining techniques such as association rules or sequential patterns on access log files. Once interesting patterns are discovered, we illustrate how they can be used to customize the server hypertext organization dynamically.

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