Metadata-Version: 2.1
Name: clustering-benchmarks
Version: 1.1.4
Summary: A Framework for Benchmarking Clustering Algorithms
Home-page: https://clustering-benchmarks.gagolewski.com/
Download-URL: https://github.com/gagolews/clustering-benchmarks
Author: Marek Gagolewski
Author-email: marek@gagolewski.com
Maintainer: Marek Gagolewski
License: GNU Affero General Public License v3
Project-URL: Bug Tracker, https://github.com/gagolews/clustering-benchmarks/issues
Project-URL: Documentation, https://clustering-benchmarks.gagolewski.com/
Project-URL: Source Code, https://github.com/gagolews/clustering-benchmarks
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
License-File: LICENSE

<a href="https://clustering-benchmarks.gagolewski.com"><img src="https://www.gagolewski.com/_static/img/clustbench.png" align="right" height="128" width="128" /></a>
# [A Framework for Benchmarking Clustering Algorithms](https://clustering-benchmarks.gagolewski.com/)

Maintained/edited/authored by [Marek Gagolewski](https://www.gagolewski.com).

This project aims to:

* **aggregate, polish, and standardise the existing clustering benchmark
    batteries** referred to across the machine learning and data mining
    literature,
* introduce **new datasets** of different dimensionalities,
    sizes, and cluster types,
* propose a **consistent methodology** for evaluating clustering algorithms.

See <https://clustering-benchmarks.gagolewski.com/> for a detailed description.

**How to cite**:
Gagolewski M., A framework for benchmarking clustering algorithms,
*SoftwareX* **20**, 2022, 101270, <https://clustering-benchmarks.gagolewski.com>,
DOI: [10.1016/j.softx.2022.101270](https://doi.org/10.1016/j.softx.2022.101270).
