K-means Clustering Result Always Changes
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Alvi Syahrin
am 4 Mai 2013
Kommentiert: Walter Roberson
am 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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Akzeptierte Antwort
Walter Roberson
am 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 Kommentare
esmat abdallah
am 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
am 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);
Weitere Antworten (2)
the cyclist
am 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
am 14 Sep. 2017
I am facing the same issue inconsistency in the output of fcm. Can anyone help me
3 Kommentare
Mehmet Volkan Ozdogan
am 28 Mär. 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
am 29 Mär. 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.
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