Tabular Foundation Models Can Learn Association Rules

TL;DR

This paper introduces TabProbe, a novel framework using tabular foundation models to learn association rules directly from data, overcoming scalability issues and maintaining high performance even with limited data.

Contribution

It proposes a model-agnostic, TFM-based framework for association rule learning that eliminates the need for frequent itemset mining and demonstrates effectiveness across various data sizes.

Findings

TFMs produce concise, high-quality rules

Rules have strong predictive performance

Method remains robust in low-data regimes

Abstract

Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning framework that extracts association rules from any conditional probabilistic model over tabular data, enabling us to leverage TFMs. We then introduce TabProbe, an instantiation of our framework that utilizes TFMs as conditional probability estimators to learn association rules out-of-the-box without frequent itemset mining. We evaluate our approach on tabular datasets of varying sizes based on standard ARM rule quality metrics and downstream classification performance. The results show that TFMs consistently produce concise, high-quality association rules with strong predictive performance and remain robust in low-data settings without task-specific training. Source code is available at https://github.com/DiTEC-project/tabprobe.