JMLR

CLASSIX: A High-Performance Library for Explainable Clustering

Authors
Xinye Chen Stefan Güttel Kaustubh Roy
Research Topics
Machine Learning
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Paper Information
  • Journal:
    Journal of Machine Learning Research
  • Added to Tracker:
    Nov 08, 2026
Abstract

We present CLASSIX, an open-source Python library for fast and explainable clustering. The library provides a unified interface to three metric-specific implementations, compiled acceleration with portable Python fallbacks, and extensive user and developer documentation. CLASSIX accepts NumPy arrays and Pandas DataFrames and is available under the MIT license at https://github.com/nla-group/classix.

Author Details
Xinye Chen
Author
Stefan Güttel
Author
Kaustubh Roy
Author
Research Topics & Keywords
Machine Learning
Research Area
Citation Information
APA Format
Xinye Chen , Stefan Güttel & Kaustubh Roy . CLASSIX: A High-Performance Library for Explainable Clustering. Journal of Machine Learning Research .
BibTeX Format
@article{paper1850,
  title = { CLASSIX: A High-Performance Library for Explainable Clustering },
  author = { Xinye Chen and Stefan Güttel and Kaustubh Roy },
  journal = { Journal of Machine Learning Research },
  url = { https://www.jmlr.org/papers/v27/26-1057.html }
}
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