CALA-FOMF: a continuous action-set learning automata-based approach to finding optimized membership functions for fuzzy association rules in web usage data

Web usage data usually contain quantitative values, and this implies that fuzzy logic can be used to represent such values. The time spent by users on each web page is a part of web usage data, which can be used to analyze users’ browsing behavior. In existing research on fuzzy web mining, the time duration of web pages is shown as trapezoidal membership functions (TMFs), and the number and parameters of TMFs are already predefined. TMFs of each web page are different from those of other web pages. Therefore, instead of using predefined TMFs, in this study, we proposed a new algorithm called CALA-FOMF to find both the number of TMFs and their optimized parameters to mine fuzzy association rules in web usage data using a team of continuous action-set learning automata (CALA). CALA-FOMF contained two steps. In the first step, using a team of CALA, we introduced a new framework. The proposed framework obtained the number of TMFs as inputs and found their optimized parameters. The proposed framework was able to reduce the search space and eliminate inappropriate membership functions during the learning process. In the second step, we proposed a new algorithm using the proposed framework to find an appropriate number of TMFs and their optimized parameters. The performance of the CALA-FOMF approach was compared with that of the fuzzy web mining algorithm, which used uniform TMFs. Experiments on datasets with different sizes confirmed that the proposed CALA-FOMF increased the efficiency of mining fuzzy association rules by extracting optimized TMFs.