# ARULESPY: Exploring Association Rules and Frequent Itemsets in Python

arXiv: https://arxiv.org/abs/2305.15263 · HTML: https://mlsanity.com/paper/2305.15263/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 2 of 2)

- [Exploring the Trie of Rules: a fast data structure for the representation of association rules](https://mlsanity.com/paper/2310.17355/) (2023)
- [Improving LLMs with a knowledge from databases](https://mlsanity.com/paper/2506.05560/) (2025)

## Abstract (arXiv)

The R arules package implements a comprehensive infrastructure for representing, manipulating, and analyzing transaction data and patterns using frequent itemsets and association rules. The package also provides a wide range of interest measures and mining algorithms, including the code of Christian Borgelt's popular and efficient C implementations of the association mining algorithms Apriori and Eclat, and optimized C/C++ code for mining and manipulating association rules using sparse matrix representation. This document describes the new Python package arulespy, which makes this infrastructure available for Python users.