摘要： 为同时获得正负量化关联规则,并尽量减少人为干预的影响,在多目标烟花优化算法的基础上,提出一种正负量化关联规则挖掘算法。引入全面搜索关联规则,使用外部库存放非支配解,通过基于相似度的冗余淘汰机制保持库中关联规则的多样性,经多次迭代获得关联规则集合。实验结果表明,该算法无需人为指定支持度、置信度等阈值,一次运行后即可获得正负关联规则。此外,与Apriori算法及单目标进化算法相比,该算法在不同数据集上均可得到稳定的结果,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。

关键词: 正负量化关联规则, 多目标优化, 烟花算法, Pareto最优, 精英保留策略

Abstract: To obtain positive and negative quantitative association rules simultaneously and reduce the impact of human intervention as far as possible,a mining algorithm of positive and negative quantitative association rules based on multi-objective firework optimization algorithm is proposed.It introduces comprehensive search association rules and uses external library to store non-dominated solution.The diversity of association rules in the library is maintained by means of the redundancy elimination mechanism based on similarity.After several iterations,the association rule set is obtained.Results of experiments on real dataset show that there is no need for the proposed algorithm to specify the thresholds of support or confidence,and both positive and negative quantitative association rules can be mined in only one single step.In contrast with Apriori algorithm and single objective evolutionary algorithms,this algorithm gives stable results upon different real-world datasets,and the dataset can be fully covered.At the same time,a better trade-off between reliability,relevance and comprehensibility is achieved.

Key words: positive and negative quantitative association rules, multi-objective optimization, firework algorithm, Pareto optimality, elitism reservation strategy

中图分类号: 

• TP301.6