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.

[1]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  S. Kanmani,et al.  Population Based Search Methods in Mining Association Rules , 2012 .

[3]  Rajib Mall,et al.  Application of elitist multi-objective genetic algorithm for classification rule generation , 2008, Appl. Soft Comput..

[4]  Wenbo Xu,et al.  An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position , 2008, Appl. Math. Comput..

[5]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[6]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[7]  S. Kanmani,et al.  Rule Acquisition in Data Mining Using a Self Adaptive Genetic Algorithm , 2011, CSE 2011.

[8]  James A. Foster,et al.  The efficient set GA for stock portfolios , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[9]  Caihui Liu,et al.  Multi-dimension association rule mining based on Adaptive Genetic Algorithm , 2011, 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering.

[10]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

[11]  Jing Liu,et al.  Parameter Selection of Quantum-Behaved Particle Swarm Optimization , 2005, ICNC.

[12]  Wenbo Xu,et al.  Adaptive parameter control for quantum-behaved particle swarm optimization on individual level , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[13]  Xueming Ding,et al.  A Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization , 2011, Eng. Appl. Artif. Intell..

[14]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[15]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .