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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. <\/jats:p>","DOI":"10.1142\/s0218001421590266","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T01:49:39Z","timestamp":1612403379000},"page":"2159026","source":"Crossref","is-referenced-by-count":5,"title":["Finding Suitable Membership Functions for Mining Fuzzy Association Rules in Web Data Using Learning Automata"],"prefix":"10.1142","volume":"35","author":[{"given":"Zohreh","family":"Anari","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Information Technology, Payame Noor University (PNU), P. O. 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