Genetic algorithm with a structure-based representation for genetic-fuzzy data mining

Mining association rules is an important data mining technology aiming to find the relationship among items in the databases. Genetic-fuzzy data mining uses evolutionary algorithm, such as genetic algorithm (GA), to optimize the membership functions for mining fuzzy association rules, and has received considerable success. The increase in data, especially in big data analytics, poses serious challenges to GA in the effectiveness and efficiency of finding appropriate membership functions. This study proposes a GA for enhancing genetic-fuzzy mining of association rules. First, we design a novel chromosome representation considering the structures of membership functions. The representation facilitates arrangement of membership functions. Second, this study presents two heuristics in the light of overlap and coverage for removing inappropriate arrangement. A series of experiments is conducted to examine the proposed GA on different amounts of transactions. The experimental results show that GA benefits from the proposed representation. The two heuristics help to explore the structures of membership functions and achieve significant improvement on GA in terms of solution quality and convergence speed. The satisfactory outcomes validate the high capability of the proposed GA in genetic-fuzzy mining of association rules.

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