基于多目标烟花算法的关联规则挖掘

Association Rules Mining Based on Multi-objective Fireworks Optimization Algorithm

针对量化关联规则的特点,提出基于多目标烟花算法和反向学习的量化关联规则挖掘算法.该算法通过多目标烟花算法全面搜索关联规则,引入反向学习提高算法收敛速度并降低算法陷入局部最优的概率,使用基于相似度的冗余淘汰机制保持库中关联规则的多样性,经过多次迭代最终获得关联规则集合.文中算法无需人为指定支持度、置信度等阈值,实验表明,算法在不同数据集上均获得稳定结果,能充分覆盖数据集,在可靠性、相关性及可理解性之间获得较好的均衡.

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.