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2011

Enhancing particle swarm optimization using chaotic operators for association rule mining

Author:K.Indira and S.Kanmani

Abstract:
Association Rule (AR) mining is a data mining task that discovers interesting relations between variables in databases. The main focus of research in association rule mining is on improving the computational efficiency of mining the rules. The conventional methods available for mining association rules depend on the threshold values of minimum support and minimum confidence. The setting up of these values needs great care and knowledge about the application domain. This paper deals with mining association rules using chaotic Particle swarm optimization (cPSO). Particle swarm optimization (PSO) is simple but powerful population based search technique for solving optimization problems. Chaotic PSO modifies the velocity updation function by introducing chaotic operators derived from chaotic maps. Both PSO and chaotic PSO generate AR with consistency and better computational accuracy when tested on three datasets. The range of attribute values in the dataset is found to affect the performance of the system.