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different output in kmeans

Asked by Elysi Cochin on 6 May 2013

i used kmeans for clustering similar images.... if i run the code first i get the correct clusters..... but without closing matlab if i execute the second time for the same image, it is clustering different output.... why like that..... what shud i do to get the same output whenever i execute the code... please do reply.....


Elysi Cochin


2 Answers

Answer by Youssef KHMOU on 6 May 2013
Accepted answer

hi, i think this question has been asked before, the reason is that the K-means algorithm starts with random partition so every time you run the code, you get the same result but with different RMSE.

(try to clear the Workspace and re-run ...)


José-Luis on 6 May 2013

@Youssef: no, you don't get the same result, if by result you mean centers. It might happen that you get the same RMSE for different centers. That's rather unlikely though. Or the same centers and different RMSE, but that's also very unlikely.

Walter Roberson on 7 May 2013

Could you indicate

size(repmat(minc, nsamp, 1))
size( bsxfun(@times, (0:nsamp-1).', (maxc - minc) ./
  (nsamp-1)) )
Youssef  KHMOU
Answer by José-Luis on 6 May 2013
Edited by José-Luis on 6 May 2013

An option is to reset the random number generator to its initial state every time before running your code:

rng default % ->This is the important bit
X = [randn(100,2)+ones(100,2);...
opts = statset('Display','final');
[idx,ctrs] = kmeans(X,2,...

This will always produce the same result, but it sorts of beat the purpose of the function and might produce bad results.


José-Luis on 6 May 2013

For example:

X = [randn(100,2)+ones(100,2);...
opts = statset('Display','final');
[idx,ctrs] = kmeans(X,2,...
                    'start',[0.25 0.25; 0.75 0.75]);

But that does not guarantee that the result will always be the same.

Elysi Cochin on 7 May 2013

thank you all for your valuable suggestions in helping me to solve my problem..... thank you all once again......


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