A clustering algorithm with genetically optimized membership functions for fuzzy association rules mining

In this paper, we propose genetic algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit within an interval of user specified minimum support values. This is achieved by tuning the base values of the membership functions for each quantitative attribute so as to maximize the sum of large itemsets in a certain interval of minimum support values. To the best of our knowledge, this is the first effort in this direction. To support our claim, we compare the proposed GAs-based approach with a CURE-based approach. Experimental results on synthetic transactions show that the proposed clustering method exhibits a good performance over CURE-based approach in terms of the number of produced large itemsets and interesting association rules.