An Evolutionary Quantum Behaved Particle Swarm Optimization for Mining Association Rules

In data mining, association rule mining is a popular and well researched method for discovering interesting relations between variables in large databases, which are meaningful to the users and can generate strong rules on the basis of these frequent patterns, which are helpful in decision support system. Quantum Particle Swarm Optimization (QPSO) is one of the several methods for mining association rules. It combines the aspects of traditional PSO philosophy and quantum mechanics. However, preventing the occurrence of local optima and improving the convergence speed is still a tedious task. In this paper, an Evolutionary Quantum behaved Particle Swarm Optimization (EQPSO) is presented with improved computational efficiency and has proper convergence. The proposed work introduces local search techniques into QPSO using Modified Shuffled Frog Leaping Algorithm (MSFLA) and depicts a systematic parameter adaptation by developing an Evolutionary State Estimation (ESE) and an Elitist Learning Strategy (ELS). The EQPSO implementation has comprehensively been evaluated on 5 different datasets taken up from the UCI Irvine repository. The performance of EQPSO is compared with Basic QPSO and the experimental results shows that the proposed system outperforms the existing algorithm quite significantly. KeywordsAssociation Rule Mining, Elitist, Evolutionary State, Memetic, Particle Swarm Optimization, Quantum Behavior, Self-Adaptive, Shuffled Frog Leaping. ——————————  ——————————