Dave, I used daisy with the default settings (daisy(M) where M is the matrix).
Henrik On 11 June 2010 21:57, Dave Roberts <dvr...@ecology.msu.montana.edu> wrote: > Henrik, > > The clustering algorithms you refer to (and almost all others) expect > the matrix to be symmetric. They do not seek a graph-theoretic solution, > but rather proximity in geometric or topological space. > > How did you convert y9oru matrix to a dissimilarity? > > Dave Roberts > > Henrik Aldberg wrote: > >> I have a directed graph which is represented as a matrix on the form >> >> >> 0 4 0 1 >> >> 6 0 0 0 >> >> 0 1 0 5 >> >> 0 0 4 0 >> >> >> Each row correspond to an author (A, B, C, D) and the values says how many >> times this author have cited the other authors. Hence the first row says >> that author A have cited author B four times and author D one time. Thus >> the >> matrix represents two groups of authors: (A,B) and (C,D) who cites each >> other. But there is also a weak link between the groups. In reality this >> matrix is much bigger and very sparce but it still consists of distinct >> groups of authors. >> >> >> My problem is that when I cluster the matrix using pam, clara or agnes the >> algorithms does not find the obvious clusters. I have tried to turn it >> into >> a dissimilarity matrix before clustering but that did not help either. >> >> >> The layout of the clustering is not that important to me, my primary >> interest is the to get the right nodes into the right clusters. >> >> >> >> Sincerely >> >> >> Henrik >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> 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. >> > > - > [[alternative HTML version deleted]] ______________________________________________ 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.
