Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering

We propose an automated clustering method based on multiobjective genetic algorithms (GA); the aim of this method is to automatically cluster values of a given quantitative attribute to obtain large number of large itemsets in low duration (time). We compare the proposed multi-objective GA-based approach with CURE-based approach. In addition to the autonomous specification of fuzzy sets, experimental results showed that the proposed automated clustering exhibits good performance over CURE-based approach in terms of runtime as well as the number of large itemsets and interesting association rules.