Abstract: Action Mining is a sub-field of Data Mining that concerns about finding ready-to-apply action rules. The majority of the patterns discovered by traditional data mining methods require analysis and further work by domain experts to be applicable in target domain while Action Mining methods try to find final cost-effective actions that can be applied immediately in target domain. Current state-of-the-art methods in AM domain only consider discrete attributes for action rule mining. Therefore, one should discretize continuous attributes using traditional discretization methods before using them for action rule mining. In this paper, the concept of Fuzzy Action Rule has been introduced. In this type of action rule, continuous attributes can be presented in fuzzy form. So that they can suggest fuzzy changes for continuous attributes instead of discretizing them. Because the space of all fuzzy action rules can be so huge a Genetic Algorithm-based Fuzzy Action Rule Mining (GA-FARM) method has been devised for finding the most cost-effective fuzzy action rules with tractable complexity. The proposed method has been implemented and tested on different real datasets. Results confirm that the proposed method is successful in finding cost-effective fuzzy action rules in acceptable time.

Zahra Entekhabi and Pirooz Shamsinejadbabki, “FARM: Fuzzy Action Rule Mining” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090134

@article{Entekhabi2018,
title = {FARM: Fuzzy Action Rule Mining},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090134},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090134},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {1},
author = {Zahra Entekhabi and Pirooz Shamsinejadbabki}
}