Info
此问题已关闭。 请重新打开它进行编辑或回答。
k means clustering: Classifying subsequent to a particular cluster - can it be done?
1 次查看(过去 30 天)
显示 更早的评论
Hi there,
I have question in relation to k means clustering. Say I created two clusters from data. For example using this code:
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);
plot(X(idx==1,1),X(idx==1,2),'r.','MarkerSize',12)
hold on
plot(X(idx==2,1),X(idx==2,2),'b.','MarkerSize',12)
plot(ctrs(:,1),ctrs(:,2),'kx',...
'MarkerSize',12,'LineWidth',2)
plot(ctrs(:,1),ctrs(:,2),'ko',...
'MarkerSize',12,'LineWidth',2)
legend('Cluster 1','Cluster 2','Centroids',...
'Location','NW')
My question is, if you collect more data can you assign it to each of the two clusters that have already been formed, or do you have to cluster all of the data again?
If it is possible, how would you do it?
Thank you
0 个评论
回答(1 个)
Wayne King
2011-11-16
k-means is an unsupervised learning algorithm that is sensitive to the number of clusters you choose AND to the initial start centers. I would say that you would need to cluster the data again.
0 个评论
此问题已关闭。
另请参阅
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!