Efficient K-Means Clustering using JIT

Version (2.02 KB) by Yi Cao
A simple but fast tool for K-means clustering
Updated 16 Apr 2008

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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 (2024). Efficient K-Means Clustering using JIT (https://www.mathworks.com/matlabcentral/fileexchange/19344-efficient-k-means-clustering-using-jit), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

correct bugs in examples