Evolutionary Multi-Objective Optimization Framework for Mining Association Rules

• URL: http://arxiv.org/abs/2003.09158v1
• Date: Fri, 20 Mar 2020 09:27:53 GMT
• Title: Evolutionary Multi-Objective Optimization Framework for Mining Association Rules
• Authors: Shaik Tanveer Ul Huq and Vadlamani Ravi
• Abstract summary: Two multi-objective optimization frameworks 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) Our study suggests that NSGA-III-ARM framework works better than MOEAD-ARM framework in both the variants across majority of the datasets.
• Score: 5.010425616264462
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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