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Re: st: cluster analysis validation
"Dasinger, Lisa" <email@example.com>
Re: st: cluster analysis validation
Tue, 17 Apr 2012 11:36:07 -0700
Thank you, Brendan,
I've downloaded your programs, and I will try your suggested solution.
What is the input for the ari command? I don't see a help file for it.
Would it be the dataset variable (old vs. new) and cluster group
variable (with values 1..6)? Also, I see that the permtab command seems
to require a square matrix, but I would have a 6 x 2 matrix.
Date: Mon, 16 Apr 2012 15:57:30 -0700
From: "Dasinger, Lisa" <firstname.lastname@example.org>
Subject: st: cluster analysis validation
I've run a cluster analysis in Stata 11.2 based on three continuous
variables using -cluster wardslinkage- with the default
similarity/dissimilarity measure to generate 6 groups. I'd like to know
if there is a way to apply the same cluster analysis to a new set of
data. In other words, is there a way to run a new dataset through the
old cluster analysis and see how new observations are classified, akin
to running a regression equation and then taking a new dataset to obtain
out of sample predictions?
If so, is there a way to evaluate how well the "old" analysis fits the
new data, e.g., by determining how similar/dissimilar each new
observation is to the observations in the cluster in which each is
placed, and whether the new observation is placed in the "best" cluster,
meaning the one that minimizes the distance between the observation and
the "center" of the existing cluster?
I am new to cluster analysis and am looking for a way to validate the
"old" cluster analysis.
Lisa Dasinger, Ph.D.
Data Reporting Manager
Zenith Insurance Company
Pleasanton Regional Office
4309 Hacienda Drive, Suite 200
Pleasanton, CA 94588
Date: Tue, 17 Apr 2012 00:54:03 +0100
From: email@example.com (Brendan Halpin)
Subject: Re: st: cluster analysis validation
On Mon, Apr 16 2012, Dasinger, Lisa wrote:
> I've run a cluster analysis in Stata 11.2 based on three continuous
> variables using -cluster wardslinkage- with the default
> similarity/dissimilarity measure to generate 6 groups. I'd like to
> if there is a way to apply the same cluster analysis to a new set of
> data. In other words, is there a way to run a new dataset through the
> old cluster analysis and see how new observations are classified, akin
> to running a regression equation and then taking a new dataset to
> out of sample predictions?
I would suggest pooling the two data sets, running a new cluster
analysis, and analysing the resultant 6*2 table (cluster classification
by old/new). That would test the extent to which the two data sets are
similarly distributed across a joint classification. If that's
acceptable (and the combined data set is small enough) it is a clean and
If you are concerned that the joint classification is not compatible
with the old classification, you can compare the old cluster membership
with the joint cluster membership, for the old data. A good measure of
agreement is the Adjusted Rand Index. Comparing cluster solutions is
tricky because they don't have "labels" -- there is no way of saying
that a given group in classification A is the same as any particular
group in classification B, apart from having shared membership. The ARI
takes that into account.
In theory you can relate the new data to the old classification by
calculating the distance from each new observation to the old cluster
centroids, but I don't know an easy way of doing that with Stata.
PS: I have code to estimate the ARI, and to re-arrange cluster solutions
to maximise similarity. If you are interested, check out:
net from http:teaching.sociology.ul.ie/sadi
net install sadi
and look at the -ari- and -permtab- commands.
Brendan Halpin, Department of Sociology, University of Limerick,
Tel: w +353-61-213147 f +353-61-202569 h +353-61-338562; Room F1-009
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