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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.

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 21 Apr 2018
Hi,
this is my input.

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