I would add to the fine advice so far that you can increase the iterations
in K-means (maybe from the default of 10 to 20). This will help stabilize
the problem of sort dependence.
On Mon, May 17, 2010 at 12:34 PM, john wurst <firstname.lastname@example.org>wrote:
> Another approach is to use both hierarchical and K-Means (tandem
> approach). Run hierarchical (Wards is a common method) and use the group
> centroids as the starting seeds for K-means.
> -----Original Message-----
> *From:* SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU]*On Behalf Of
> *Art Kendall
> *Sent:* Monday, May 17, 2010 11:54 AM
> *To:* SPSSX-L@LISTSERV.UGA.EDU
> *Subject:* Re: K-Means Cluster Analysis
> k-means is very dependent on the order the cases are in. One expects
> slightly different results with addition or omission of a few cases or
> changes in the sort order of the cases.
> That is why I have long recommended that a user sort the cases in a few
> random orders and checks the consistency of the assigned cluster
> *Are you getting different results with identical input?*
> Also TWOSTEP is a step up from the older k-means approach.
> What is the nature of your data? Kinds of variables? relative independence
> of the variables? what kind of entity is the case?
> Art Kendall
> Social Research Consultants
> On 5/17/2010 10:15 AM, Courtney M. Cronley wrote:
> I am running a K-means cluster analysis with 12 clusters, and I keep getting different clusters. For example, yesterday Cluster 2 had two cases in it. Today Cluster 2 has one case in it. Any ideas about why this is the case?
> Thanks in advance for any advice that anyone can offer.
> Courtney Cronley, Ph.D.
> Postdoctoral Associate
> Center of Alcohol Studies
> Rutgers Universitycronley@rci.rutgers.edu
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