Efficient K-Means Clustering using JIT
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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.
Free download from Shareware Connection - This is a tool for K-means clustering.
Version: 1.0 | Size: 10 KB | Platform: Matlab, Scripts
Released Date: 15-03-2013 | Rating: 0 | Title: Efficient K-Means Clustering using JIT
Author Url: http://www.mathworks.com
Program Info Url: http://www.mathworks.com
Download Url: http://www.mathworks.com/matlabcentral/fx_files/19344/1/k_means.zip
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