论文标题
采矿协会规则的进化多目标优化框架
Evolutionary Multi-Objective Optimization Framework for Mining Association Rules
论文作者
论文摘要
在本文中，提出了两个多目标优化框架（即NSGA-III-ARM-V1，NSGA-IIII-ARM-V2;和MOEAD-ARM-V1，MOEAD-ARM-V2）中的两个多目标优化框架（即，提出了NSGA-III-ARM-V1，NSGA-II-ARM-V1，MOEAD-ARM-V2）。第一个框架使用非主导的分类遗传算法III（NSGA-III），第二个框架使用基于分解的多目标进化算法（MOEA/D）来找到多样的，非冗余和非主导的关联规则（具有高度的目标函数值）。在这两个框架中，都无需指定最低支持和最低信心。在第一个变体中，支持，信心和提升被视为客观功能，而第二，信心，提升和趣味性被视为客观功能。这些框架对七种不同类型的数据集进行了测试，包括两个真实的银行数据集。我们的研究表明，在大多数数据集的两个变体中，NSGA-III-ARM框架的效果都比Moead-Arm框架更好。
In this paper, two multi-objective optimization frameworks in two variants (i.e., NSGA-III-ARM-V1, NSGA-III-ARM-V2; and MOEAD-ARM-V1, MOEAD-ARM-V2) are proposed to find association rules from transactional datasets. The first framework uses Non-dominated sorting genetic algorithm III (NSGA-III) and the second uses Decomposition based multi-objective evolutionary algorithm (MOEA/D) to find the association rules which are diverse, non-redundant and non-dominated (having high objective function values). In both these frameworks, there is no need to specify minimum support and minimum confidence. In the first variant, support, confidence, and lift are considered as objective functions while in second, confidence, lift, and interestingness are considered as objective functions. These frameworks are tested on seven different kinds of datasets including two real-life bank datasets. Our study suggests that NSGA-III-ARM framework works better than MOEAD-ARM framework in both the variants across majority of the datasets.
