PyAerial: Scalable association rule mining from tabular data

Scalable association rule mining from tabular data using the Aerial neurosymbolic method. PyAerial provides a comprehensive toolkit for association rule mining with advanced capabilities: Scalable Rule Mining - Efficiently mine association rules from large tabular datasets without rule explosion Automatic Quality Metrics - Rules include support, confidence, Zhang's metric, and more calculated automatically Frequent Itemset Mining - Generate frequent itemsets with support values using the same neural approach ARM with Item Constraints - Focus rule mining on specific features of interest Classification Rules - Extract rules with target class labels for interpretable inference Numerical Data Support - 8 built-in discretization methods (unsupervised: equal-frequency, equal-width, k-means, quantile, custom bins; supervised: entropy-based, ChiMerge, decision tree) Customizable Architectures - Fine-tune autoencoder layers and dimensions for optimal performance GPU Acceleration - Leverage CUDA for faster training on large datasets Comprehensive Metrics - Support, confidence, lift, conviction, Zhang's metric, Yule's Q, interestingness Rule Visualization - Integrate with NiaARM for scatter plots and visual analysis Flexible Training - Adjust epochs, learning rate, batch size, and noise factors CITATION: If you use PyAerial in your research, please cite our papers: The neurosymbolic method paper:Karabulut, E., Groth, P., & Degeler, V. (2025). Neurosymbolic Association Rule Mining from Tabular Data. In Proceedings of The 19th International Conference on Neurosymbolic Learning and Reasoning (NeSy 2025), PMLR 284:565-588.https://proceedings.mlr.press/v284/karabulut25a.html The software paper:Karabulut, E., Groth, P., & Degeler, V. (2025). PyAerial: Scalable association rule mining from tabular data. SoftwareX, 31, 102341.https://doi.org/10.1016/j.softx.2025.102341