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This is a tool for K-means clustering. After trying several different ways to program, I got the conclusion that using simple loops to perform distance calculation and comparison is most efficient and accurate because of the JIT acceleration in MATLAB.
The code is very simple and well documented, hence is suitable for beginners to learn k-means clustering algorithm.
Numerical comparisons show that this tool could be several times faster than kmeans in Statistics Toolbox.
Cite As
Yi Cao (2026). Efficient K-Means Clustering using JIT (https://www.mathworks.com/matlabcentral/fileexchange/19344-efficient-k-means-clustering-using-jit), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired: Patch color selector
General Information
- Version 1.0.0.0 (2.02 KB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0.0.0 | correct bugs in examples |
