Multi-dimensional sequential web mining by utilizing fuzzy interferencing

There are several applications of sequential web mining, which is used to find the frequent subsequences in a web log in the World Wide Web (the web). We implemented a tool to analyze the sequential behavior of web log access patterns in multiple-dimensions. Sequences of frequent access patterns may change temporally and spatially. Based on the specified criteria like year, month, day, hours and location, the end-user is able to tune the minimum support threshold parameter intuitively using the fuzzy inference mechanism. Domain experts are can access several criteria, including minimum support threshold and number of accesses according to the user intuition, which is later, transformed into fuzzy inference parameters. We propose two different types of rule bases by considering the (support-minimum support, minimum support) and (support, minimum support), i.e., interval and case-based. To test our proposal, we used the web log dataset of the Department of Computer at the University of Calgary to analyze sequential access patterns of students during February and March carried out in the campus by taking the midterm dates into account. The results reported in this paper are promising; they demonstrate the applicability and effectiveness of the proposed approach.