[R] kmeans cluster analysis. How do I (1) determine probability of cluster membership (2) determine cluster membership for a new subject

[John Sorkin]

Ranjan,
Thank you for your help. What eludes me is how one computes the distance from each cluster for each subject. For my first subject, datascaled[1,], I have tried to use the following: 
v1 <- sum(fit$centers[1,]*datascaled[1,])
v2 <- sum(fit$centers[2,]*datascaled[1,])
v3 <- sum(fit$centers[2,]*datascaled[1,])
hoping the max(v1,v2,v3) would reproduce the group assignment, i.e. simply assign the subject to the group that gives the largest value, but it does not. How is the distance to the three clusters computed for each subject?
Thanks,
John 
 



 
 
John David Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)>>> Ranjan Maitra <maitra.mbox.ignored at inbox.com> 10/2/2012 1:59 PM >>>
John,

On Tue, 2 Oct 2012 11:35:12 -0400 John Sorkin
<jsorkin at grecc.umaryland.edu> wrote:

> Window XP
> R 2.15
>  
> I am running a cluster analysis in which I ask for three clusters (see code below). The analysis nicely tells me what cluster each of the subjects in my input dataset belongs to. I would like two pieces of information
> (1) for every subject in my input data set, what is the probability of the subject belonging to each of the three cluster

K-means provides hard clustering, whatever cluster has closest mean
gets the assignment.

> (2) given a new subject, someone who was not in my original dataset, how can I determine their cluster assignment?

Look at the distance between the subject the cluster means: the one
that is closest gets assigned the cluster.

If you are looking for probabilistic clustering (under Gaussian
mixture model assumptions), you could use model-based clustering: one R
package is mclust.

Btw, note that kmeans is very sensitive to initialization (as is
mclust): you may want to try several random starts (for kmeans),
at the very least. Use the argument "nstart" with a huge number.

HTH,
Ranjan


> Thanks,
> John
>  
> # K-Means Cluster Analysis
> jclusters <- 3
> fit       <- kmeans(datascaled, jclusters) # 3 cluster solution
>  
> and fit$cluster tells me what cluster each observation in my input dataset belongs to (output truncated for brevity):
>  
> > fit$cluster   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17 . . . .
>   1   1   1   1   3   1   1   1   1   2   1   2   1   1   1   1   1 . . . . How do I get probability of being in cluster 1, cluster 2, and cluster 3 for a given subject, e.g datascaled[1,]?How do I get the cluster assigment for a new subject?Thanks,John 
> John David Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> University of Maryland School of Medicine Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
> Baltimore, MD 21201-1524
> (Phone) 410-605-7119
> (Fax) 410-605-7913 (Please call phone number above prior to faxing)
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