Optimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining

The transactions in web data often consist of quantitative data, suggesting that the fuzzy set theory can be used to represent such data. the time spent by the users on each web page is one type of web data, regarded as a trapezoidal membership function (tmf), and can be used to evaluate the user browsing behavior. quality of the mining fuzzy association rules depends on the membership functions, and since the membership functions of each web page are different from those for the other web pages, the automatic finding of the number and position of tmf is significant. in this paper, a different reinforcement-based optimization approach called la-omf is proposed to find both the number and position of tmfs for the fuzzy association rules. in the proposed algorithm, the centers and spreads of tmfs are considered as the parameters of the search space, and a new representation using learning automata (la) is proposed to optimize these parameters. the performance of the proposed approach is evaluated, and the results obtained are compared with the results of the other algorithms on a real dataset. experiments on the datasets with different sizes confirm that the proposed la-omf approach improves the efficiency of mining fuzzy association rules by extracting the optimized membership functions.