Fast K-means clustering

Version 1.7.0.0 (119 KB) by Sebastien PARIS
Fast mex K-means clustering algorithm with possibility of K-mean++ initialization.
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Updated 17 May 2021

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Fast mex K-means clustering algorithm with possibility of K-mean++ initialization
(mex-interface modified from the original yael package https://gforge.inria.fr/projects/yael)
- Accept single/double precision input
- Support of BLAS/OpenMP for multi-core computation
Please run mexme_kmeans.m to compile mex-files (be sure that mex -setup have been done at least one)
Run demo "test_yael_kmeans.m"

Cite As

Sebastien PARIS (2026). Fast K-means clustering (https://uk.mathworks.com/matlabcentral/fileexchange/33541-fast-k-means-clustering), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Categories
Find more on Statistics and Machine Learning Toolbox in Help Center and MATLAB Answers
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Acknowledgements

Inspired: Sparsified K-Means, Ziheng_GMM.zip

Version Published Release Notes
1.7.0.0

Fix mwIndex pointers for modern Matlab. Currently works flawless when compiled with Intel compiler and linked with MKL.
1.6.0.0

- Fix compilation issue in mexme_yael_kmeans
- Include both mexw32 & mexw64 files in two separate files (unzip them in local dir in case of problem)
1.5.0.0

- Fix a bug in ndellipse introduced in the last update
1.4.0.0

-Correct a bug in mexme_yael_kmeans.m for Linux/Mac Os
1.3.0.0

-Correct a bug in randperm
1.2.0.0

- Minor changes
- Add spiral clustering example in the test file
1.1.0.0

- Add online help, minor changes
1.0.0.0