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.

中文翻译：

---

使用学习自动机寻找合适的隶属函数以挖掘 Web 数据中的模糊关联规则

网络数据中的交易是海量数据，通常由模糊和定量的值组成。挖掘模糊关联规则有助于发现 Web 数据之间有趣的关系。这些规则的好坏取决于隶属函数，因此，找到合适的隶属函数数量和位置至关重要。用户在每个网页上花费的时间，显示了他们对这些网页的兴趣程度，可以被认为是梯形隶属函数（TMF）。在本文中，优化问题是为每个网页找到合适的 TMF 数量和位置。为了解决这个优化问题，提出了一种基于学习自动机的算法来优化TMF的数量和位置（LA-ONPTMF）。