Basically the random forest algorithm can generate a proximity 
matrix of the data, and it's up to you how you would want to 
proceed from there.  You can feed that into clustering 
algorithms that accept a similarity matrix, or turn it into a 
distance matrix for clustering algorithms that need a distance
matrix (e.g., hclust()).  You may or may not want to do 
ordination as the UCLA folks suggest.

I think this is one of the great things about working in R:
you have the freedom to choose how you want to proceed from
some intermediate result, and not locked in to something some
one decide to hardwire into the software.

Andy

From: Gavin Simpson
> 
> On Wed, 2007-08-15 at 09:44 -0700, David Katz wrote:
> > Several searches turned up nothing. Perhaps I will try to 
> implement it if
> > nobody else has. Thanks.
> 
> You can do this with Andy Liaw's randomForest package can do this and
> the first hit on a Google search (on term "RFclustering") was this:
> 
> http://www.genetics.ucla.edu/labs/horvath/RFclustering/RFclust
> ering.htm
> 
> which shows how one might go about this with some helper functions.
> 
> G
> 
> -- 
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>  Gavin Simpson                 [t] +44 (0)20 7679 0522
>  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
>  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk
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