Extracting Membership Functions Using ACS Method via Multiple Minimum Supports

Ant Colony Systems (ACS) have been successfully applied to different optimization issues in recent years. However, only few works have been done by employing ACS method to data mining. This paper addresses the lack of investigations on this study by proposing an ACS -based algorithm to extract membership functions in fuzzy data mining. In this paper, the membership functions were encoded into binary bits, and then they have given to the ACS method to discover the optimum set of membership functions. By considering this approach, a comprehensive exploration can be executed to implement the system automation. Therefore, it is a new frontier, since the proposed model does not require any user-specified threshold of minimum support. Hence, we evaluated our approach experimentally and could reveal this approach by significant improving of membership functions. Keywordsfuzzy data mining; multiple minimum supports; association rule; membership functions; ant colony system.