Integrating multi-objective genetic algorithms into clustering for fuzzy association rules mining

Abstract

In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We compare the proposed multi-objective GA based approach with: 1) CURE based approach; 2) Chien et al clustering approach. Experimental results on 100K transactions extracted from the adult data of United States census in year 2000 show that the proposed method exhibits good performance over the other two approaches in terms of runtime, number of large itemsets and number of association rules.