Résumé
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. Exhibiting relationships and global patterns that exist in these large files, but are hidden among the vast amounts of data is usually published in this context. Nevertheless, the large amount of input data poses a maintenance problem. In fact, maintening global patterns iss à non-trivial task after access log file update because new data may invalidate old client behavior and creates new ones. In this paper we address the problem of incremental web usage mining, i.e. the problem of mining user patterns when new transactions or new clients are added to the original access log file.