A Novel Imperialist Competitive Algorithm for Automated Mining of Association Rules
Fariba Khademolghorani
Journal of Intelligent Computing, Vol. 2, No. 3, 2011.
DOI: 
Link: http://www.dline.info/jic/fulltext/v2n3/4.pdf
Abstract:
Association rule mining can be considered as an optimization problem. A lot of algorithms have been introduced in the area, but they suffer from several limitations. Recently, imperialist competitive algorithm (ICA) has been introduced for solving different optimization problems. This paper proposes a novel ICA algorithm for automated mining of the interesting and readable association rules without considering the minimum support and the minimum confidence thresholds. In this algorithm, the convergence rate and the computational efficiency of ICA have been improved. These modifications on ICA includes modification of the modeling the assimilation policy and combining it with a mutation operator of a genetic algorithm, which lead to increasing the exploration of the algorithm and on the other hand lead to the improvement of the convergence rate. The value of the mutation probability is automatically determined without requiring to be specified by the user. The experimental results indicate the efficiency of this algorithm in comparison with the methods of mining association rules based on the basic ICA and the genetic algorithm. Thus, these modifications are not only useful for association rule mining, but also it can be extended to other optimization problems.