How to perform k-means algorithm in MATLAB?

Question

I'm new to matlab and I want to know how to perform k-means algorithm in MATLAB, and also I want to know how to define cluster centers when performing k means.

For example, let's say I'm creating an array as given below.

m = [11.00; 10.60; 11.00; 12.00; 11.75; 11.55; 11.20; 12.10; 11.60; 11.40;...
     11.10; 11.60; 11.00; 10.15; 9.55; 9.35; 8.55; 7.65; 7.80; 8.45; 8.05]

I want to cluster the above values into 5 clusters where k = 5.

And I would like to take the cluster centers as 2, 5, 10, 20, 40.

So my question is how can I define the cluster centers and perform k-means algorithm in MATLAB? Is there a specific parameter to set the cluster centers in MATLAB kmeans() function?

Please help me to solve the above problems.

Answer 1

The goal of k-means clustering is to find the k cluster centers to minimize the overall distance of all points from their respective cluster centers.

With this goal, you'd write

[clusterIndex, clusterCenters] = kmeans(m,5,'start',[2;5;10;20;40])

This would adjust the cluster centers from their start position until an optimal position and assignment had been found.

If you instead want to associate your points in m to fixed cluster centers, you would not use kmeans , but calculate the clusterIndex directly using min

distanceToCenter = bsxfun(@minus,m,[2 5 10 20 40]);
[~, clusterIndex] = min(abs(distanceToCenter),[],2); 

ie. you calculate the difference between every point and every center, and find, for each point, the center with the minimum (absolute) distance.

---

To plot results, you can align the points on a line and color them according to the center:

nCenters = length(clusterCenters);
cmap = hsv(nCenters);
figure
hold on
for iCenter = 1:nCenters
    plot(m(iCenter==clusterIndex),1,'.','color',cmap(iCenter,:));
    plot(clusterCenters(iCenter),1,'*','color',cmap(iCenter,:));
end

Answer 2

You can see a classic implementation in my post here - K-Means Algorithm with Arbitrary Distance Function Matlab (Chebyshev Distance) .

Enjoy...