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## Efficient K-Means Clustering using JIT

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A simple but fast tool for K-means clustering

Updated

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.

Ningamma Husenappa

provoid code

Junqi WANG

Nikolay S.

leila

### leila (view profile)

Does the code support 3d data?

S.Karthi

Maxime

### Maxime (view profile)

Although not a perfect way to solve the above-mentioned issue, adding the following two lines after the update of the centroids solved the problem in my case:

idnan = find(isnan(c(:,1)));
c(idnan,:) = X(randi(n,length(idnan),1),:);

Maxime

### Maxime (view profile)

Pretty fast indeed!

However, the number of cluster is sometimes not respected. The algorithm yields a lower number of clusters, replacing additional centroid by NaN. This can be inconvenient.

Tim Benham

### Tim Benham (view profile)

The function fails to terminate on some inputs. For example see http://snipt.org/wpkI

Nandha

Edgar Goederer

### Edgar Goederer (view profile)

The code is very nice and well documented. In some cases, however, the clusters are not properly identified if no initial centroid vectors are provided. This could be improved by automatically trying a small number of different random initial guesses and chosing the configuration which yields the smallest sum of distance between points and centroids.

V. Poor

Mo Chen

### Mo Chen (view profile)

nicola rebagliati

this stuff works and examples/comparisons are given

 27 Mar 2008 update description
##### MATLAB Release
MATLAB 7.5 (R2007b)