Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata

Author
Keywords
Abstract
Association 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.
Year of Publication
2022
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
7
Issue
Regular Issue
Number
4
Number of Pages
27-43
Date Published
06/2022
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2022-05/ijimai_7_4_3.pdf
DOI
10.9781/ijimai.2022.01.001
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