Zohreh Anari

Transactions in web data are huge amounts of data, often consisting of fuzzy and quantitative values. Mining fuzzy association rules can help discover interesting relationships between web data. The quality of these rules depends on membership functions, and thus, it is essential to find the suitable number and position of membership functions. The time spent by users on each web page, which shows their level of interest in those web pages, can be considered as a trapezoidal membership function (TMF). In this paper, the optimization problem was finding the appropriate number and position of TMFs for each web page. To solve this optimization problem, a learning automata-based algorithm was proposed to optimize the number and position of TMFs (LA-ONPTMF). Experiments conducted on two real datasets confirmed that the proposed algorithm enhances the efficiency of mining fuzzy association rules by extracting the optimized TMFs.

As the number of web pages increases, search for useful information by users on web sites will become more significant. By determining the similarity of web pages, search quality can be improved; hence, users can easily find their relevant information. In this paper, distributed learning automata and probabilistic grammar were used to propose a new hybrid algorithm in order to specify the similarity of web pages by means of web usage data. In the proposed algorithm, a Learning Automata (LA) for each web page is assigned which its function is to evaluate association rules extracted by hypertext system. This learning process continues until the similarity of web pages are determined. Experimental results demonstrate the efficiency of the proposed algorithm over other existing techniques.