K-means Clustering Result Always Changes
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Alvi Syahrin
le 4 Mai 2013
Commenté : Walter Roberson
le 26 Nov 2021
I'm working on k-means in MATLAB. Here are my codes:
load cobat.txt
k=input('Enter the number of cluster: ');
if k<8
[cidx ctrs]=kmeans(cobat, k, 'dist', 'sqEuclidean');
Z = [cobat cidx]
else
h=msgbox('Must be less than eight');
end
"cobat" is the file of mine and here it looks:
65 80 55
45 75 78
36 67 66
65 78 88
79 80 72
77 85 65
76 77 79
65 67 88
85 76 88
56 76 65
My problem is everytime I run the code, it always shows different result, different cluster. How can I keep the clustering result always the same?
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Walter Roberson
le 5 Mai 2013
%generate some initial cluster centers according to some deterministic algorithm
%in this case, I construct a space-diagonal equally spaced, but choose your
%own algorithm
minc = min(cobat, 1);
maxc = max(cobat, 1);
nsamp = size(cobat,1);
initialcenters = repmat(minc, nsamp, 1) + bsxfun(@times, (0:nsamp-1).', (maxc - minc) ./ (nsamp-1));
%Once you have constructed the initial centers, cluster using those centers
[cidx ctrs] = kmeans(cobat, k, 'dist', 'sqEuclidean', 'start', initialcenters);
6 commentaires
esmat abdallah
le 26 Nov 2021
initialcenters = repmat(minc, nsamp, 1) + bsxfun(@times, (0:nsamp-1).', (maxc - minc) ./ (nsamp-1));
please, matlab out an error on this line : "Error using +
Matrix dimensions must agree."
what can i do ??
Walter Roberson
le 26 Nov 2021
%generate some initial cluster centers according to some deterministic algorithm
%in this case, I construct a space-diagonal equally spaced, but choose your
%own algorithm
minc = min(cobat, [], 1);
maxc = max(cobat, [], 1);
nsamp = size(cobat,1);
initialcenters = repmat(minc, nsamp, 1) + bsxfun(@times, (0:nsamp-1).', (maxc - minc) ./ (nsamp-1));
%Once you have constructed the initial centers, cluster using those centers
[cidx ctrs] = kmeans(cobat, k, 'dist', 'sqEuclidean', 'start', initialcenters);
Plus de réponses (2)
the cyclist
le 4 Mai 2013
K-means clustering uses randomness as part of the algorithm Try setting the seed of the random number generator before you start. If you have a relatively new version of MATLAB, you can do this with the rng() command. Put
rng(1)
at the beginning of your code.
Pallavi Saha
le 14 Sep 2017
I am facing the same issue inconsistency in the output of fcm. Can anyone help me
3 commentaires
Mehmet Volkan Ozdogan
le 28 Mar 2019
Hi,
I have a question about rng(). If we use rng() command, K-means algortihm stil repeats until the results are getting convergenced to the best. Is that right?
Thank you
Walter Roberson
le 29 Mar 2019
Yes.
rng(SomeParticularNumericSeed)
just ensures that it will always use the same random number sequence provided that no other random numbers are asked for between the rng() call and the kmeans call.
Voir également
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