I am trying to use k-means in order to get the euclidean distance for each point to the cluster centroid location.
I did the followings:
M=dlmread('Matrix.txt',' ') --> Here is where I load all my data to be clustered
[IDX,C] = kmeans(M,10) --> Getting my centroid location in C with k=10
X=dlmread('Data.txt',' ') --> Where I load one part of my data
Then, I run the following:
[IDX,C,sumd,D] = kmeans(X,k) --> to get the distances in D
but it will give an error message as I need to supply a value for k.
If I do:
[IDX,C,sumd,D] = kmeans(X,10)
then my C (or the centroid location) will change.
How can I preserve my cluster centroid location and obtain the distances for each point of X in this case?
Thank you. I look forward to your clarification.
Subject:k-means From:Tom Lane Date: 16 Sep, 2011 19:51:19 Message: 2 of 3
> [IDX,C] = kmeans(M,10) --> Getting my centroid location in C with k=10
> [IDX,C,sumd,D] = kmeans(X,10)
> then my C (or the centroid location) will change.
Fred, it sounds like you want to compute C from M, then just assign points
in X to one of the C rows based on the distance -- not do any re-clustering.
I think you just want to compute the distances and find the minimum
directly. Try this:
x = [rand(5,2);1+rand(5,2)]; % some data
c = [.5 .5; 1.5 1.5]; % the cluster centroids I want
d = pdist2(x,c); % compute distances from each point to each centroid
[~,cc] = min(d,,2); % get the centroid that achieves the minimum
After sending the e-mail below, I found that:
accomplish the same thing like pdist2 mentioned below.
Am I right here?
Thanks, Tom for the help.
"Tom Lane" <email@example.com> wrote in message <firstname.lastname@example.org>...
> > [IDX,C] = kmeans(M,10) --> Getting my centroid location in C with k=10
> > [IDX,C,sumd,D] = kmeans(X,10)
> > then my C (or the centroid location) will change.
> Fred, it sounds like you want to compute C from M, then just assign points
> in X to one of the C rows based on the distance -- not do any re-clustering.
> I think you just want to compute the distances and find the minimum
> directly. Try this:
> x = [rand(5,2);1+rand(5,2)]; % some data
> c = [.5 .5; 1.5 1.5]; % the cluster centroids I want
> d = pdist2(x,c); % compute distances from each point to each centroid
> [~,cc] = min(d,,2); % get the centroid that achieves the minimum
> -- Tom
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