Improving the referrer-based Web log session reconstruction

Web usage mining, an emergent domain in Web mining, is the process of discovering and interpreting patterns of users´ accessing of the Web by mining the Web log data. We discuss the kinds of ambiguities a heuristic encounters during the process of user session reconstruction. Some criteria are introduced to measure these ambiguities. The proposed criteria are then used to adapt a reconstructor heuristic to the conditions of a particular server log. We extend the conventional referrer-based heuristic to exploit this adaptation in order to achieve higher performance as well as a reasonable stability. The results of simulation experiments are presented and analyzed in order to show the performance achievements and the stability tradeoff.