A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy

In the past, we proposed a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions based on the divide-and-conquer strategy. In this paper, an enhanced approach, called the cluster-based genetic-fuzzy mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It first divides the chromosomes in a population into k clusters by the A-means clustering approach and evaluates each individual according to its own information and the information of the cluster it belongs to. The final best sets of membership functions in all the populations are then gathered together for mining fuzzy association rules. Experimental results also show the effectiveness and efficiency of the proposed approach.