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Scikit-mine : pattern mining in Python

• Descriptive analysis, leading to interpretable, concise descriptions using the Minimum Description Length Principle
• Fast Algorithms
• Simple, extendable API, inspired by scikit-learn

• Free software: BSD license
• GitHub: https://github.com/scikit-mine/scikit-mine
• Documentation: https://scikit-mine.github.io/scikit-mine/

scikit-mine is a Python module for pattern mining built on top of Pandas/Numpy/SciPy and is distributed under the 3-Clause BSD license.

It is currently maintained by a team of volunteers.

See examples in the tutorials; the notebooks are available here. To execute the tutorials, you will have to install jupyter notebook or jupyterlab (https://jupyter.org/install)

scikit-mine requires Python>=3.8, and some extra dependencies

• scipy>=1.2.1
• pandas>=1.0.0
• pyroaring>=0.3.4
• joblib>=0.11.1
• sortedcontainers>=2.1.0
• dataclasses>=0.6
• networkx
• wget>=3.2
• scikit-learn
• graphviz
• matplotlib
• pydot

This project benefitted from fundings from the INRIA center in Rennes, Brittany, France, as well as from the CNRS PNRIA Programme.

We welcome new contributors of all experience levels.

• Rémi Adon (https://github.com/remiadon)
• Hermann Courteille (https://github.com/hermann74)
• Cyril Regan (https://github.com/cyril-data)
• Thomas Betton (https://github.com/thomasbtnfr)
• Esther Galbrun (https://github.com/nurblageij)
• Peggy Cellier (https://github.com/PeggyCellier)
• Alexandre Termier (https://github.com/alexandre-termier)
• Luis Galárraga (https://github.com/lgalarra)
• Josie Signe (https://github.com/Darlysia)
• Francesco Bariatti (https://github.com/fbariatti)
• Mensah-David Assigbi (https://github.com/davidassigbi)
• Arnauld-Cyriaque Djedjemel (https://github.com/Ariaque)