different output in kmeans
2 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
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.....
0 commentaires
Réponse acceptée
Youssef Khmou
le 6 Mai 2013
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 ...)
5 commentaires
Walter Roberson
le 7 Mai 2013
Could you indicate
size(repmat(minc, nsamp, 1))
size( bsxfun(@times, (0:nsamp-1).', (maxc - minc) ./
(nsamp-1)) )
Plus de réponses (1)
José-Luis
le 6 Mai 2013
Modifié(e) : José-Luis
le 6 Mai 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);...
randn(100,2)-ones(100,2)];
opts = statset('Display','final');
[idx,ctrs] = kmeans(X,2,...
'Distance','city',...
'Replicates',5,...
'Options',opts);
This will always produce the same result, but it sorts of beat the purpose of the function and might produce bad results.
2 commentaires
José-Luis
le 6 Mai 2013
Modifié(e) : José-Luis
le 6 Mai 2013
For example:
X = [randn(100,2)+ones(100,2);...
randn(100,2)-ones(100,2)];
opts = statset('Display','final');
[idx,ctrs] = kmeans(X,2,...
'Distance','city',...
'Replicates',1,...
'Options',opts,...
'start',[0.25 0.25; 0.75 0.75]);
But that does not guarantee that the result will always be the same.
Voir également
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!