There is a considerable body of work on sequence mining of Web Log Data We are using One Pass frequent Episode discovery (or FED) algorithm, takes a different approach than the traditional apriori class of pattern detection algorithms. In this approach significant intervals for each Website are computed first (independently) and these interval used for detecting frequent patterns/Episode and then the Analysis is performed on Significant Intervals and frequent patterns That can be used to forecast the user's behavior using previous trends and this can be also used for advertising purpose. This type of applications predicts the Website interest. In this approach, timeseries data are folded over a periodicity (day, week, etc.) Which are used to form the Interval? Significant intervals are discovered from these time points that satisfy the criteria of minimum confidence and maximum interval length specified by the user.
Saxena, et al., REF integrated mining and analysis by proposing the One Pass Frequent Episode discovery (FED) algorithm.
Significant Interval and Frequent Pattern Discovery in Web Log Data
International Journal of Computer Science Issues, IJCSI, Vol. 7, Issue 1, No. 3, January 2010, http://ijcsi.org/articles/Significant-Interval-and-Frequent-Pattern-Discovery-in-Web-Log-Data.php
Computer Science