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Re: Pspp-users Digest discussion of cluster assignment scores


From: Mark Levine
Subject: Re: Pspp-users Digest discussion of cluster assignment scores
Date: Thu, 12 Sep 2013 13:11:59 -0400

Hi folks,

I have to respectfully disagree in part with Fredrik's comments. 

I totally agree with him that K-Means does not provide a unique solution. 
Repeated clustering to determine the stability of a solution is wise council.

I think, however, that Frederik is off re the relationship between clustering 
and discriminant analysis. In discrim, you start with cases that have been 
assigned a priori to groups and the analysis seeks to find optimal (linear) 
combinations of predictor variables that properly assign cases to the groups. 
It's sort of like multiple regression with a nominal dependent variable. Well, 
sort of . . .

What Jason has requested (as I have in a previous note) is a vector of scores 
that identifies which cases fall into which cluster. One can then look at the 
distinguishing characteristics of each cluster by cross-tabbing, AOVing, etc. 
the cluster assignment score against other variables.

Without the cluster assignment scores for further analysis, doing K-Means -- or 
any kind of clustering procedure, e.g. Q-type factor analysis -- is somewhere 
between relatively and totally useless.

At least in my not so humble estimation.

Cheers,

Mark


On Sep 12, 2013, at 12:00 PM, address@hidden wrote:

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Today's Topics:

  1. PSPP K-means quick-cluster: assigning cases (Fredrik Clementz)


----------------------------------------------------------------------

Message: 1
Date: Thu, 12 Sep 2013 14:02:01 +0200
From: Fredrik Clementz <address@hidden>
To: address@hidden, "address@hidden"
        <address@hidden>
Subject: PSPP K-means quick-cluster: assigning cases
Message-ID:
        <address@hidden>
Content-Type: text/plain; charset="iso-8859-1"

Hi Jason,

The feature you're looking for is Discriminant analysis and is
unfortunately not implemented in PSPP. I also have to mention that you
should be vary about using K-means as a clustering technique. Please
rerandomize data and run several times to see if results are similar as the
technique is dependant on how the data i sorted.

Cheers,

-- 
Fredrik
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