BPSO Algorithm with Opposition-Based Learning Method for Association Rule Mining

Traditional association rule mining algorithm does not operate efficiently when processing large and high-dimensional data. With an opposition-based learning method, a binary particle swarm optimization algorithm is proposed to improve the association rule mining problem. The proposed method uses a binary particle swarm algorithm to search for association rules and does not need to manually specify support and confidence thresholds. In addition, opposition-based learning is introduced, and the primary and the secondary opposition-based learning methods are used to reduce the probability of the algorithm falling into local extreme and improve the convergence accuracy of the algorithm. The experimental results show that the improved algorithm converges faster than existing algorithms and balances multiple indexes of reliability, correlation, and comprehensibility, thus mining more effective association rules.