Extracting membership functions by ACS algorithm without specifying actual minimum support

Ant Colony Systems (ACS) have been successfully utilized 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 given to the ACS to search for the optimal set of membership functions. With the proposed algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. We experimentally evaluate our approach and reveal that our algorithm significantly improve membership functions and reduce the computation costs.