You may want to consider a k-means cluster.  The pvclust appears to be a hierarchical clustering algorithm (with subsequent p value estimation) which is causing the problem.

Hierarchical clustering uses a pairwise distance matrix to form the tree dendrogram.  With N = 238804 this will require a matrix with N(N-1)/2 or about (238804^2)/2 elements.  That's what causes the memory problem.

K-means is not so intensive and will result in clustering the 238804 rows (I assume they are snp's) and each cluster will be represented by a men vector for the 49 variables.

If on the other hand you want to cluster the 49 columns you may need to transpose the data matrix and then run a hierarchical clustering, but I would look into kmeans first.

Bill Shannon
Washington Univ. School of Medicine


ssls sddd <ssls.sddd@gmail.com> wrote: Dear List,

I have a question to bother you about how to do clustering.
My data consists of 49 columns (49 variables) and 238804 rows.
I would like to do hierarchical clustering (unsupervised clustering
and PCA). So far I tried pvclust
(www.is.titech.ac.jp/~shimo/prog/
*pvclust*/)
but I always had the problem like for R like "cannot allocate the memory".

I am curious about what else packages can perform the clustering analysis
while memory efficient.

Meanwhile, is there any way that I can extract the features of each cluster.

In other words, I would like to identify which are responsible for
classifying these
variables (samples).

Thanks a lot!

Sincerely,

Alex

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