01903nas a2200253   4500000000100000000000100001008004100002260001200043653005100055653001600106653002000122653001000142653002800152653002200180100001300202700001600215700001300231245011600244856008000360300001000440490000600450520117900456022001401635       2022                            d  c06/202210aContinuous Action-set Learning Automata (CALA)10aData Mining10aFuzzy Rule Base10aFuzzy10aFuzzy Association Rules10aLearning Automata1 aZ. Anari1 aA. Hatamlou1 aB. Anari00aAutomatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata  uhttps://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_3.pdf  a27-430 v73 aAssociation rule mining is an important data mining technique used for discovering relationships among all data items. Membership functions have a significant impact on the outcome of the mining association rules. An important challenge in fuzzy association rule mining is finding an appropriate membership functions, which is an optimization issue. In the most relevant studies of fuzzy association rule mining, only triangle membership functions are considered. This study, as the first attempt, used a team of continuous action-set learning automata (CALA) to find both the appropriate number and positions of trapezoidal membership functions (TMFs). The spreads and centers of the TMFs were taken into account as parameters for the research space and a new approach for the establishment of a CALA team to optimize these parameters was introduced. Additionally, to increase the convergence speed of the proposed approach and remove bad shapes of membership functions, a new heuristic approach has been proposed. Experiments on two real data sets showed that the proposed algorithm improves the efficiency of the extracted rules by finding optimized membership functions.  a1989-1660