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Kmeans clustering in k=10

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Ali Ali
Ali Ali on 18 Apr 2018
Commented: Ali Ali on 21 Apr 2018
I have a matrix with (256*1707) and I want to cluster it with Kmeans with k=10, and plot it..?
I appreciate any help you can provide.

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Answers (1)

njj1
njj1 on 18 Apr 2018
Edited: njj1 on 18 Apr 2018

1) Randomly initialize 10 cluster centroids. This can be done by simply randomly selecting 10 points from your dataset.

2) Compute the distance (Euclidean, presumably) from each data point to these 10 centroids.

3) Assign cluster membership of each point to the cluster who's centroid is the closest.

4) Re-compute centroid of each cluster

5) Compute distance from each data point to the 10 centroids.

6) So on...

Plotting:

for i=1:10
     plot(matrix(cluster==i,dim1),matrix(cluster==i,dim2),'o')
     hold on
end

In this plot, you have to choose two dimensions to plot against each other. From the looks of it, you have either 256 or 1707 dimensions (aka features).

  17 Comments

Ali Ali
Ali Ali on 18 Apr 2018
Maybe it will work, but this is an image and converted to a matrix, and I have to plot all of its pixels.
Image Analyst
Image Analyst on 19 Apr 2018
Ali, attach your data in a .mat file if you want more help, to make it easier for people to help you.
Also, you've marked it solved/accepted, so are you all done with this question?
Ali Ali
Ali Ali on 21 Apr 2018
Hi,
this is my input.

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