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. -- --------------------------------- 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 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
