Features

• Fuzzy clustering: FCM, PCM, GK, SCM, SISC, KFCM, PFCM, CAFCM, and related variants

• Probabilistic and mixture models: GMM, LDA, PLSI, MBMM, MMSB, BGMM

• Rough and possibilistic clustering: RoughKMeans, WBSC, NOCD, DMon, RDFKC

• Nonnegative matrix factorization and graph clustering: BayesianNMF, BIGCLAM, CDCgS, SoftDBSCANGM

• Document and graph clustering methods: KMART, SCSPA and additional soft clustering algorithms

Installation

The complete package, including deep learning-based algorithms, can be installed from PyPI using:

pip install soft-clustering[deep]

For users who only need the core algorithms (without deep learning dependencies), install the base version:

pip install soft-clustering

Quick Start

Basic usage with the package API:

import numpy as np
from soft_clustering import FCM

X = np.array([[1.0, 2.0], [1.1, 2.1], [8.0, 8.1], [8.2, 7.9]])
model = FCM(m=2.0, max_iter=150, tol=1e-5)
memberships = model.fit_predict(X, K=2)
centers = model.centers_

print("Soft membership shape:", memberships.shape)
print("Cluster centers:", centers)

Documentation

Full API reference, examples, algorithm descriptions, and usage guides are available in the official documentation:

https://soft-clustering.readthedocs.io/en/latest/index.html

Testing

The project includes a comprehensive test suite in the tests/ directory, covering all implemented algorithms.

To run the tests:

pip install -e ".[deep,dev]"   # Install in development mode
pytest

See tests/HOW_TO_RUN.txt for more details.

Research & Citation

This package is part of an academic research project submitted to the JMLR MLOSS journal. A publication is forthcoming.

• Paper link: https://arxiv.org/abs/2607.19620

• Citation:

@misc{rezaee2026scppunifiedpythonlibrary,
   title={SCPP: A Unified Python Library for Soft Clustering},
   author={Kiyan Rezaee and Morteza Ziabakhsh and Artin Bahrampour and Seyed Mohammad Ghoreishi and Asal Khaje and Ali Sajedifar and Manny Chalak and Ava Zerafatangiz and Sadegh Eskandari},
   year={2026},
   eprint={2607.19620},
   archivePrefix={arXiv},
   primaryClass={cs.LG},
   url={https://arxiv.org/abs/2607.19620},
}

Contributing

Contributions, bug reports, and feature requests are welcome. Please open issues or pull requests on the repository.

Please see our:

• Contributing Guidelines

• Code of Conduct

License

Distributed under the terms of the MIT license.