  Association rule mining is one of the most studied research fields of data
mining, with applications ranging from grocery basket problems to explainable
classification systems. Classical association rule mining algorithms have
several limitations, especially with regards to their high execution times and
number of rules produced. Over the past decade, neural network solutions have
been used to solve various optimization problems, such as classification,
regression or clustering. However there are still no efficient way association
rules using neural networks. In this paper, we present an auto-encoder solution
to mine association rule called ARM-AE. We compare our algorithm to FP-Growth
and NSGAII on three categorical datasets, and show that our algorithm discovers
high support and confidence rule set and has a better execution time than
classical methods while preserving the quality of the rule set produced.
