Date: Mon, 26 Sep 2005 10:22:53 -1000
Reply-To: Bob Schacht <email@example.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Bob Schacht <firstname.lastname@example.org>
Subject: Re: Cluster Analysis - best practices
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At 04:32 AM 9/26/2005, cristiano wrote:
> I'm a statistician but I'm looking for some books/resources/example for
>using Cluster Analysis with SPSS: i'd like to know the models and methods
>behind this analysis.
> In your experience, could you suggest to me some stuff?
> Thanks in advance
As a prelude to your reading, let me comment in general. Cluster analyses
fall into two approaches: One is polythetic agglomerative in nature, the
other monothetic subdivisive.
Polythetic agglomerative methods start with every case as an individual,
and proceed to cluster by combining cases that most closely resemble each
other. In each step of the analysis, the similarity between remaining cases
and clusters is measured, and those most closely resembling each other are
combined. This proceeds by steps as far as one wants to go, based on
measures of cohesion or similarity.
Monothetic subdivisive methods, on the other hand, start with all cases
combined into one supergroup. The procedure in this case is how to
subdivide the supergroup in to two groups in a way that maximizes the
*difference* between the two groups. I'm not clear on how this procedure
works, but it may begin with variables with the highest degree of
variability, and splitting the cases at the mean. Again, the process
proceeds stepwise until some threshold criterion is reached.
You may have some a priori reason for preferring one approach over the
other. Descriptions of the methods may not identify themselves clearly with
these alternatives, so this overview might prove helpful.
Robert M. Schacht, Ph.D. <email@example.com>
Pacific Basin Rehabilitation Research & Training Center
1268 Young Street, Suite #204
Research Center, University of Hawaii
Honolulu, HI 96814