Henrik,
Given your initial matrix, that should tell you which authors are
similar/dissimilar to which other authors in terms of which authors they
cite. In this case authors 1 and 3 are most similar because they both
cite authors 2 and 4. Authors 2 and 3 are most different because they
both cite 6 authors but none of the same authors
(sqrt(6^2+5^2+1^2)=7.87). 1 and 2 are next most different because 1
only cites 5 authors but shares none with 2 (sqrt(6^2+4^2+1^2)=7.28) etc.
If you want to know which authors are similar in terms of who gas
cited them, simply transpose the matrix
daisy(t(M))
I'm guessing none of this is actually what you are looking for
however, and Etienne's graph theoretic approach may be more what you
have in mind.
Dave
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
David W. Roberts office 406-994-4548
Department of Ecology email drobe...@montana.edu
Montana State University
Bozeman, MT 59717-3460
Henrik Aldberg wrote:
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
<mailto: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
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