ARULESPY: Exploring Association Rules and Frequent Itemsets in Python
Databases
2023-05-25 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2305.15263,
title = {ARULESPY: Exploring Association Rules and Frequent Itemsets in Python},
author = {Michael Hahsler},
journal= {arXiv preprint arXiv:2305.15263},
year = {2023}
}