> Requires: T = cluster(X): A hierarchical clustering algorithm > L = cut-tree(T; k): produces a partition with k non-singleton clusters
The functions you'll want to read the documentation to, here, are "hclust()" and "cutree()". They're fairly straightforward and nicely documented. It looks like you are running cutree at multiple heights of the hclust object to get different numbers of clusters; am I interpreting that correctly? --elijah > s(L1;L2): a similarity between two partitions > > 1: f = 0:8 > 2: T =cluster(X) fthe reference clustering > 3: for i = 1 to num subsamples do > 4: subi =subsamp(X; f) fsub-sample a fraction f of the datag > 5: Ti=cluster(subi) > 6: end for > 7: for k = 2 to kmax do > 8: L1=cut-tree(T; k) fpartition the reference clusteringg > 9: for i = 1 to maximum iterations do > 10: L2 =cut-tree(Ti; k) > 11: S(i; k) = s(L2;L1) computed only on the patterns of subi. > 12: end for > 13: end for > > I am glad about any help, don't really know what to do! > thanks, regards > Julia > -- > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
