Date:         Mon, 26 Sep 2005 20:25:07 -0300
Reply-To:     Hector Maletta <hmaletta@fibertel.com.ar>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Hector Maletta <hmaletta@fibertel.com.ar>
Subject:      Re: Cluster Analysis - best practices
Comments: To: Aric Zion <Aric.Zion@asu.edu>
In-Reply-To:  <CD78388FAE6FCA4DA5A345BC93CA1C6D34C7F2@ex0.asurite.ad.asu.edu>
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A good introduction is Brian Everitt, Cluster Analysis (Arnold, London and
Halsted Press, New York).
At a higher level of sophistication, K.Jajuga, A. Sokolowski and H.-H. Bock,
editors, Classification, Clustering and Data Analysis (Springer).
About using SPSS clustering procedures, you better follow Marija Norusis'
advice.

Hector

> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU]
> On Behalf Of Aric Zion
> Sent: Monday, September 26, 2005 8:21 PM
> To: SPSSX-L@LISTSERV.UGA.EDU
> Subject: Re: Cluster Analysis - best practices
>
> A clear overview is provided by H. Charles Romesburg's
> "Cluster Analysis for Researchers", (2004) Lulu Press.  While
> this doesn't give guidance on how to specifically run Cluster
> Analysis in SPSS, it does offer an very clear view of how
> cluster analysis operates.
>
> Aric
>
>
>         -----Original Message-----
>         From: SPSSX(r) Discussion on behalf of Bob Schacht
>         Sent: Mon 9/26/2005 1:22 PM
>         To: SPSSX-L@LISTSERV.UGA.EDU
>         Cc:
>         Subject: Re: Cluster Analysis - best practices
>
>
>
>         At 04:32 AM 9/26/2005, cristiano wrote:
>         >Dear listers,
>         >  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
>         >  Cristiano
>
>         Cristiano,
>         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.
>
>         Bob
>
>
>         Robert M. Schacht, Ph.D. <schacht@hawaii.edu>
>         Pacific Basin Rehabilitation Research & Training Center
>         1268 Young Street, Suite #204
>         Research Center, University of Hawaii
>         Honolulu, HI 96814
>
>
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