Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimensional datasets often result in an excessive number of rules, increasing execution time and negatively impacting downstream task performance. Managing this rule explosion remains a central challenge in ARM research. To address this, we introduce Aerial+, a novel neurosymbolic ARM method. Aerial+ leverages an under-complete autoencoder to create a neural representation of the data, capturing associations between features. It extracts rules from this neural representation by exploiting the model's reconstruction mechanism. Extensive evaluations on five datasets against seven baselines demonstrate that Aerial+ achieves state-of-the-art results by learning more concise, high-quality rule sets with full data coverage. When integrated into rule-based interpretable machine learning models, Aerial+ significantly reduces execution time while maintaining or improving accuracy.

本研究解决了高维数据集导致的关联规则数量过多的问题，从而影响执行时间和下游任务性能。提出的Aerial+方法通过一个不完全自编码器创建数据的神经表示，提取高质量、简洁的规则集合。实验结果表明，Aerial+在执行时间上有显著提升，同时保持或改善了准确性，具有重要的应用潜力。

来自表格数据的神经符号关联规则挖掘