Association Rules Mining with Auto-Encoders
This addresses efficiency and rule quality issues in data mining for applications such as grocery basket analysis, though it appears incremental as it adapts neural networks to a known bottleneck.
The paper tackles the problem of association rule mining by proposing ARM-AE, an auto-encoder-based method that discovers high support and confidence rule sets with better execution time than classical algorithms like FP-Growth and NSGAII on three categorical datasets.
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.