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, SoftwareX 20, 2022, 101270, https://clustering-benchmarks.gagolewski.com, DOI: 10.1016/j.softx.2022.101270.
Project details
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-1.1.3.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5173275d15f4a0a14b79c20134946cfb7539b9cd1b4ab4f0ae8bd9b203b782ac |
|
MD5 | f1257c4c261475960d05329587ea542b |
|
BLAKE2b-256 | 831c17ed4c3a7756ff20c6cf9971a34b9439754490af91de1d1ccb4d9449a512 |
Close
Hashes for clustering_benchmarks-1.1.3-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 47273a1d22da3e6377a39dea00c6c8afc7939bc789107ea5dfd97b0d3c4c84b1 |
|
MD5 | 6e8fc47d06310c35cd5cc15215085a5f |
|
BLAKE2b-256 | 0e3448751d4be1567754b136874e0cb9434656d184b9933e32f55ba13b5c2b6f |