Extracting membership functions in fuzzy data mining by Ant Colony Systems

Ant colony systems (ACS) have been successfully applied to optimization problems in recent years. However, few works have been done on applying ACS to data mining. This paper proposes an ACS-based algorithm to extract membership functions in fuzzy data mining. The membership functions are first encoded into binary bits and then fed into the ACS to search for the optimal set of membership functions. An example is given to demonstrate the proposed algorithm. Numerical experiments are also made to show the performance of the proposed approach.