# Association Rules Mining with Auto-Encoders

arXiv: https://arxiv.org/abs/2304.13717 · HTML: https://mlsanity.com/paper/2304.13717/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [A deep learning approach for detecting traffic accidents from social media data](https://mlsanity.com/paper/1801.01528/) (2018)

## Built on by (most cited first, 5 of 5)

- [Neurosymbolic Association Rule Mining from Tabular Data](https://mlsanity.com/paper/2504.19354/) (2025)
- [Learning Semantic Association Rules from Internet of Things Data](https://mlsanity.com/paper/2412.03417/) (2024)
- [Discovering Association Rules in High-Dimensional Small Tabular Data](https://mlsanity.com/paper/2509.20113/) (2025)
- [Tabular Foundation Models Can Learn Association Rules](https://mlsanity.com/paper/2602.14622/) (2026)
- [AE SemRL: Learning Semantic Association Rules with Autoencoders](https://mlsanity.com/paper/2403.18133/) (2024)

## Abstract (arXiv)

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.