Multi-population Cooperative Bat Algorithm for Association Rule Mining

摘要

Association rule mining (ARM) is well-known issue in data mining. It is a combinatorial optimization problem purpose to extract the correlations between items in sizable data-sets. According to the literature study, bio-inspired prove their efficiency in term of time, memory and quality of generated rules. This paper investigates multi-population cooperative version of bat algorithm for association rule mining (BATARM) named MPB-ARM which is based on bat inspired algorithm. The advantage of bat algorithm is the power combination between population-based algorithm and the local search, however, it more powerful in local search. The main factor to judge optimization algorithms is ensuring the interaction between global diverse exploration and local intensive exploitation. To maintain the diversity of bats, in our proposed approach, we introduce a cooperative master-slave strategy between the subpopulations. The experimental results shows that our proposal outperforms other bio-inspired algorithms already exist and cited in the literature including our previous work BAT-ARM.