Why Kmeans function give us give different answer?

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I have noticed that kmeans function for one k value in a single run gives different cluster indices than while using in a loop with varying k say from 2:N. I do not understand this. It will be great if it is clear to me.

Accepted Answer

José-Luis
José-Luis on 22 Sep 2014
Because, if you are using the default settings, kmeans() randomly selects a starting point. The algorithm is not deterministic and the results might depend on that starting position.
  2 Comments
Mahesh
Mahesh on 22 Sep 2014
So what is the default setting then i have chosen:
rng('default');
Am I right?
Adam Filion
Adam Filion on 22 Sep 2014
Try using the 'replicates' option for kmeans to automatically run the algorithm multiple times and return the best answer:
>> doc kmeans
You can set the order of random numbers generated with the rng command:
>> doc rng
Putting something like rng(3) before kmeans will make the results repeatable even though it involves random starting points.

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More Answers (1)

Image Analyst
Image Analyst on 22 Sep 2014
Like many other types of numerical minimizations, the solution that kmeans reaches often depends on the starting points. It is possible for kmeans to reach a local minimum, where reassigning any one point to a new cluster would increase the total sum of point-to-centroid distances, but where a better solution does exist. However, you can use the optional 'replicates' parameter to overcome that problem.
  1 Comment
Mahesh
Mahesh on 22 Sep 2014
Yes I do understand. However, I got different answer while it is single value of cluster like
[idx,cent,sumdist] = kmeans(param_sac,nkmeans,'dist',dist_alg,...
'replicates',8, 'display','iter');
and others inside loop like
rng('default'); % For reproducibility
param_sac = load('param2W_sac.cld');
size(param_sac);
dist_alg = 'sqEuclidean';
iditer = [];
sumdistitr = [];
meansil = [];
silhitr = [];
for nkmeans = 1:10;
[idx,cent,sumdist] = kmeans(param_sac,nkmeans,'dist',dist_alg,...
'replicates',nkmeans, 'display','iter');
[silh,h] = silhouette(param_sac,idx);
xlabel('Silhouette Value')
ylabel('Cluster');
meanh = mean(silh);
iditer = [iditer idx];
% cen = [cen cent];
% sumdistitr = [sumdistitr sumdist];
meansil = [meansil; nkmeans meanh];
silhitr = [silhitr silh];
end
I got totally different in classification.
Thanks for responses to all

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