A Framework for Benchmarking Clustering Algorithms
Project description
A Framework for Benchmarking Clustering Algorithms
Maintained/edited/authored by Marek Gagolewski.
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, 2022, https://clustering-benchmarks.gagolewski.com, DOI: 10.48550/arXiv.2209.09493, under review (preprint).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for clustering-benchmarks-1.1.1.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | bcfbc20a1b3ad5ec3376581c99dc350488922964e902d0c42fc06de40ee1f4d3 |
|
MD5 | 2c6c63291610591204db7a1174fc1a87 |
|
BLAKE2b-256 | 2350f3fbac34aca13d73e23fb15554a583265681c2a09581e2d791a1a15b3c9a |
Close
Hashes for clustering_benchmarks-1.1.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | db713116c2bf37b505d6436cd54cac484f53688fe758b48e21decd5cc0ba8430 |
|
MD5 | 0038ebbaa83b07ff63915bc62412c9c1 |
|
BLAKE2b-256 | 0a43cd583fcef311f25660433b4e738e2bf4522a4d3766718abaa886306a85a4 |