https://github.com/scikit-mine/scikit-mine

scikit-mine : pattern mining in Python
https://github.com/scikit-mine/scikit-mine

datamining minimum-description-length pattern-mining scikit scikit-learn

Last synced: about 2 months ago
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scikit-mine : pattern mining in Python

• Host: GitHub
• URL: https://github.com/scikit-mine/scikit-mine
• Owner: scikit-mine
• License: bsd-3-clause
• Created: 2020-03-19T16:57:44.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2023-05-31T15:50:03.000Z (over 1 year ago)
• Last Synced: 2024-09-16T19:54:25.689Z (3 months ago)
• Topics: datamining, minimum-description-length, pattern-mining, scikit, scikit-learn
• Language: Python
• Homepage: https://scikit-mine.github.io/scikit-mine/
• Size: 33.2 MB
• Stars: 72
• Watchers: 5
• Forks: 10
• Open Issues: 9
• Metadata Files:
  • Readme: README.rst
  • Contributing: docs/CONTRIBUTING.rst
  • License: LICENSE

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README

        
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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_

.. _scikit-learn: https://scikit-learn.org/

Resources

---------

* Free software: BSD license

* GitHub: https://github.com/scikit-mine/scikit-mine

* Documentation: https://scikit-mine.github.io/scikit-mine/

Quickstart

----------

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)

.. _here: https://github.com/scikit-mine/scikit-mine/tree/master/docs/tutorials

Dependencies

------------

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

Development

-----------

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. 

Internal Contributors

---------------------

- 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)