# Discovering Categorical Main and Interaction Effects Based on Association Rule Mining

arXiv: https://arxiv.org/abs/2104.04728 · HTML: https://mlsanity.com/paper/2104.04728/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 10 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions](https://mlsanity.com/paper/1905.06501/) (2019)
- [Model Selection for High Dimensional Quadratic Regression via Regularization](https://mlsanity.com/paper/1501.00049/) (2014)
- [Reluctant Interaction Modeling](https://mlsanity.com/paper/1907.08414/) (2019)
- [Random Intersection Trees](https://mlsanity.com/paper/1303.6223/) (2013)
- [The xyz algorithm for fast interaction search in high-dimensional data](https://mlsanity.com/paper/1610.05108/) (2016)
- [BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data](https://mlsanity.com/paper/1902.03525/) (2019)

## Built on by (most cited first, 3 of 3)

- [Online Interaction Detection for Click-Through Rate Prediction](https://mlsanity.com/paper/2106.15400/) (2021)
- [Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction](https://mlsanity.com/paper/2406.17885/) (2024)
- [An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models](https://mlsanity.com/paper/2511.08077/) (2025)

## Abstract (arXiv)

With the growing size of data sets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are categorical and one-hot encoding is applied. This makes it more worthwhile mining useful features as well as their interactions. Association rule mining aims to extract interesting correlations between items, but it is difficult to use rules as a qualified classifier themselves. Drawing inspiration from association rule mining, we come up with a method that uses association rules to select features and their interactions, then modify the algorithm for several practical concerns. We analyze the computational complexity of the proposed algorithm to show its efficiency. And the results of a series of experiments verify the effectiveness of the algorithm.