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ARULESPY: Exploring Association Rules and Frequent Itemsets in Python by Hahsler, Michael is a scholarly article available to read on EtoBox.

What is ARULESPY: Exploring Association Rules and Frequent Itemsets in Python about?

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.

Author

Hahsler, Michael

Published

2023

Language

EN