SCPP: A Unified Python Library for Soft Clustering

[2607.19620] SCPP: A Unified Python Library for Soft Clustering Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2607.19620 (cs) [Submitted on 21 Jul 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: SCPP: A Unified Python Library for Soft Clustering Authors: Kiyan Rezaee , Morteza Ziabakhsh , Artin Bahrampour , Seyed Mohammad Ghoreishi , Asal Khaje , Ali Sajedifar , Manny Chalak , Ava Zerafatangiz , Sadegh Eskandari View a PDF of the paper titled SCPP: A Unified Python Library for Soft Clustering, by 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 View PDF HTML (experimental) Abstract: In this paper, we present SCPP (Soft Clustering Python Package), an open-source Python framework for soft clustering. SCPP establishes a canonical, scikit-learn-compatible estimator interface that standardizes model training, prediction, membership representation, evaluation, and benchmarking across heterogeneous soft clustering methods, including fuzzy, probabilistic, graph-based, matrix factorization, and deep learning methods. The framework currently integrates 40 representative algorithms together with a comprehensive benchmarking comprising datasets, clustering quality metrics, and standardized runtime, memory, and scalability evaluation. SCPP further provides extensive documentation, practical examples, automated testing, and seamless integration with the scientific Python ecosystem, enabling reproducible experimentation and straightforward extension with new algorithms. The source code is publicly available at this https URL . Comments: 4 pages Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2607.19620 [cs.LG] (or arXiv:2607.19620v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.19620 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Kiyan Rezaee [ view email ] [v1] Tue, 21 Jul 2026 23:01:38 UTC (1,266 KB) [v2] Sat, 15 Aug 2026 00:37:00 UTC (76 KB) Full-text links: Access Paper: View a PDF of the paper titled SCPP: A Unified Python Library for Soft Clustering, by 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 View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• SCPP: A Unified Python Library for Soft Clustering#609999
• Mutation-preserving generalized cluster algebras and Laurent mutation invariants#635978
• Mutation-preserving generalized cluster algebras and Laurent mutation invariants#633629
• AdaProb: Efficient Machine Unlearning via Adaptive Probability#128319
• Constrained Enumeration Reveals Hidden Optima and Precision-Dependent Degeneracy in Modularity-Based Community Detection#1132199