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Association rule mining through adaptive parameter control in particle swarm optimization

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  • K. Indira
  • S. Kanmani

Abstract

Association rule mining is a data mining task on a great deal of academic research has been done and many algorithms are proposed. Association rule mining is treated as a twofold process by most of the methods. It increases the complexity of the system and takes up more time and space. Evolutionary Computation (EC) are fast growing search based optimization method for association rule mining. Among ECs particle swarm optimization (PSO) is more suited for mining association rules. The bottleneck of PSO is setting the precise values for their control parameters. Setting values to the control parameter is done either through parameter tuning or parameter control. This paper proposes an adaptive methodology for the control parameters in PSO namely, acceleration coefficients and inertia weight based on estimation of evolution state and fitness value respectively. Both of the proposed adaptive methods when tested on five datasets from University of California Irvine (UCI) repository proved to generate association rules with better accuracy and rule measures compared to simple PSO. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • K. Indira & S. Kanmani, 2015. "Association rule mining through adaptive parameter control in particle swarm optimization," Computational Statistics, Springer, vol. 30(1), pages 251-277, March.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:1:p:251-277
    DOI: 10.1007/s00180-014-0533-y
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    Cited by:

    1. Cheng Maolin & Han Yun, 2017. "Application of a New Superposition CES Production Function Model," Journal of Systems Science and Information, De Gruyter, vol. 5(5), pages 462-472, October.

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