Clustering web transactions using rough approximation

Web usage mining is the application of data mining techniques to web log data repositories. Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual userʹs behavior. In general, the discovered knowledge or any unexpected rules are likely to be imprecise or incomplete, which requires a framework with soft computing techniques like rough sets. In this paper, we present a rough approximation-based clustering to cluster web transactions from web access logs. Using this approach, users can effectively mine web log records to discover web page access patterns.