[R] Dynamic clustering?
Greg.Snow at imail.org
Thu May 6 21:28:39 CEST 2010
You could do a hierarchical clustering, then look at the height of the last combination relative to the other heights, for your data:
> tmp <- hclust( dist( c(1,2,3,2,3,1,2,3,400,300,400) ) )
> tmp2 <- hclust( dist( c(400,402,405, 401,410,415, 407,412) ) )
 0 0 0 0 0 0 1 2 100 399
 1 2 2 2 5 7 15
You still need to make some assumptions and come up with a method for choosing a cutoff, but this may help get you started.
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
greg.snow at imail.org
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Ralf B
> Sent: Wednesday, May 05, 2010 3:18 PM
> To: r-help at r-project.org
> Subject: [R] Dynamic clustering?
> Are there R packages that allow for dynamic clustering, i.e. where the
> number of clusters are not predefined? I have a list of numbers that
> falls in either 2 or just 1 cluster. Here an example of one that
> should be clustered into two clusters:
> two <- c(1,2,3,2,3,1,2,3,400,300,400)
> and here one that only contains one cluster and would therefore not
> need to be clustered at all.
> one <- c(400,402,405, 401,410,415, 407,412)
> Given a sufficiently large amount of data, a statistical test or an
> effect size should be able to determined if a data set makes sense to
> be divided i.e. if there are two groups that differ well enough. I am
> not familiar with the underlying techniques in kmeans, but I know that
> it blindly divides both data sets based on the predefined number of
> clusters. Are there any more sophisticated methods that allow me to
> determine the number of clusters in a data set based on statistical
> tests or effect sizes ?
> Is it possible that this is not a clustering problem but a
> classification problem?
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