[R] clustering a sparse dissimilarity matrix

[Giuseppe Pagnoni]

Dear all,

I have a BIG dissimilarity matrix (around one thousand by one thousand), 
that I would like to cluster.  Most of the elements of the matrix are 
zero or close to zero.  Is there a way to cluster the matrix 
(hierarchical or partitioning methods) that discards those elements that 
are close to zero (by using a specified threshold on the matrix)? I am 
asking this because otherwise I get a huge amount of clutter for 
singletons or very small clusters.  Also, how can you look for clusters 
of a specified  size, apart from looking visually at the dendrogram?  Is 
there a way to bias the algorithm specifically for clusters of a certain 
size?

thank you very much for any suggestion

best regards

   giuseppe

PS
Note that I cannot use the original data instead of the dissimilarity 
matrix because those are dissimilarities (computed from the spatial 
correlation coefficient) between fMRI brain maps, each of which has 
around 60000 variables.

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Giuseppe Pagnoni
Psychiatry and Behavioral Sciences
Emory University School of Medicine
1639 Pierce Drive, Suite 4000
Atlanta, GA, 30322
tel: 404.712.8431
fax: 404.727.3233