ARULESPY: Exploring Association Rules and Frequent Itemsets in Python

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.