Abstract According to characteristics of quantitative association rules, a quantitative association rules mining algorithm based on multi-objective fireworks optimization algorithm and opposition-based learning(QAR_MOFWA_OBL) is proposed. Firstly, fireworks optimization algorithm is utilized for a complete search of association rules. Next, opposition-based learning(OBL) is introduced to improve the convergence speed of the algorithm and reduce the probability of falling into local optimum. Then, the diversity of rules is maintained by means of the elimination mechanism of redundancy. Finally, after several iterations, the association rule set is obtained. Moreover, the thresholds of support or confidence of the proposed algorithm are not expected to be specified artificially. Simulation experiment shows the stable results are obtained on different real-world datasets, and the dataset can be adequately covered with a good balance among reliability, relevance and comprehensibility.
Fund:Supported by National Natural Science Foundation of China(No.61262049), Science and Technology Research Project of Education Department of Jiangxi Province(No.GJJ13087)
About author:: (WU Qiong, born in 1991, master student. Her research interests include intelligent computing and data mining.)
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2017, Vol. 30 
