  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.
