Mining temporal web interesting patterns

Previous work on mining web associations focus primarily on finding frequent access patterns in the data. However they ignore an important relationship that web frequent access patterns have the dynamic characteristic of time varying. It is also important that in database, some items which are infrequent in whole dataset but those depend on the present of a mediator itemset may be frequent in a particular time period, which induce some interesting patterns may not be discover In this study, our focus is to apply a new mining technique called indirect association onto temporal web data and propose the TIFP-mine algorithm based on a new model WM-graph, which are both capable of extracting all temporal indirect frequent patterns and its temporal extended patterns. Experimental results confirm that TIFP-mine algorithm is efficient and effective. Our analysis shows very, promising results, especially in terms of identifying Web users with distinct interests.