Mining fuzzy association rules using a memetic algorithm based on structure representation

Abstract

The association rules render the relationship among items and have become an important target of data mining. The fuzzy association rules introduce fuzzy set theory to deal with the quantity of items in the association rules. The membership functions play a key role in the fuzzification process and, therefore, significantly affect the results of fuzzy association rule mining. This study proposes a memetic algorithm (MA) for optimizing the membership functions in fuzzy association rule mining. The MA adopts a novel chromosome representation that considers the structures of membership functions. Based on the structure representation, we develop a local search operator to improve the efficiency of the MA in exploring good membership functions. Two local search strategies for the MA are further investigated. This study conducts a series of experiments to examine the proposed MA on different amounts of transactions. The experimental results show that the MA outperforms state-of-the-art evolutionary algorithms in terms of solution quality and convergence speed. These preferable results show the advantages of the structure-based representation and the local search in improving the performance. They also validate the high capability of the proposed MA in mining fuzzy association rules.