PyAerial: Scalable association rule mining from tabular data

Association Rule Mining (ARM) is a knowledge discovery technique that identifies frequent patterns as logical implications within transaction datasets and has been applied across domains such as e-commerce, healthcare, and cyber–physical systems. However, many state-of-the-art ARM methods, typically algorithmic or nature-inspired, suffer from rule explosion and long execution times. Aerial is a novel neurosymbolic ARM algorithm for tabular datasets that mitigates rule explosion using neural networks, while remaining compatible with existing approaches. Aerial transforms tables into transactions, uses an autoencoder to learn compact neural representations, and extracts logical rules from the neural representations. This paper presents PyAerial, a Python library that makes Aerial accessible and easy to use on generic tabular datasets for end users in a domain-independent way. Besides association rules, PyAerial can also be used to extract frequent itemsets, learn classification rules, apply item constraints to learn rules over the features of interest rather than all features, pre-discretize numerical data for ARM, and can be run on a GPU.