An Optimization of Association Rule Mining Algorithm using Weighted Quantum behaved PSO

In this paper we propose Weighed Quantum behaved Particle Swarm Optimization (WQPSO) algorithm for improving the performance of association rule mining algorithm Apriori. It is a global convergence guaranteed algorithm, which outperforms original PSO algorithm and it has fewer parameters to control the search ability of PSO. Finding minimum support and minimum confidence values for mining association rules seriously affect the quality of association rule mining. In association rule mining, the minimum threshold values are always given by the user. But in this paper, WQPSO algorithm is used to determine suitable threshold values automatically and also it improves the computational efficiency of Apriori algorithm. First, the WQPSO algorithm is processed to find the minimum threshold values. In this algorithm which particle having the highest optimal fitness value, its support and confidence values are taken as the minimum threshold value to association rule algorithm. Then the minimum support and minimum confidence values are given to the input of Apriori association rule mining algorithm for mining association rules. Thus the proposed algorithm is verified by applying the FoodMart2000 database to Microsoft SQL Server 2000. The experimental results show that our proposed method gives better performance and less computational time than the existing algorithms.