Association Rules Python Packages

mlxtend

A library of extension and helper modules for Python's data analysis and machine learning libraries.

rusket

rusket 🦀🧺

efficient-apriori

An efficient Python implementation of the Apriori algorithm.

orange3-associate

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

niaarm

A minimalistic framework for Numerical Association Rule Mining

arulesviz

Arulesviz - interactive association rules vizualization tool for python

fastapriori

Faster association rules mining, even at low support

armine

Association Rule Mining package for python

arulespy

Python interface to arules for association rule mining

bluewhale3-associate

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

ba

ba (Bayesian Association) provides a unified probabilistic framework for Qualitative Comparative Analysis (QCA) and Association Rule Mining (ARM)

niaarmts

Nature-Inspired Algorithms for Time Series Numerical Association Rule Mining

fimproject

"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.

orange3-associate-zh

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

blauwal3-associate

🍊 :package: Frequent itemsets and association rules mining for Orange 3.

apriorib1

apriorib1 is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules.

fimprojects

"Frequent Mining Algorithms" is a Python library that includes frequent mining algorithms. This library contains popular algorithms used to discover frequent items and patterns in datasets. Frequent mining is widely used in various applications to uncover significant insights, such as market basket analysis, network traffic analysis, etc.