Thank you Etienne, this seems to work like a charm. Also thanks to the rest
of you for your help.
Henrik
On 11 June 2010 13:51, Cuvelier Etienne wrote:
>
>
> Le 11/06/2010 12:45, Henrik Aldberg a écrit :
>
> I have a directed graph which is represented as a matrix on the form
>>
>>
>> 0 4 0 1
Henrik,
the methods you use are NOT applicable to directed graphs, in the
contrary even. They will split up what you want to put together. In
your data, an author never cites himself. Hence, A and B are far more
different than B and D according to the techniques you use.
Please check out Etiennes
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
Dave,
I used daisy with the default settings (daisy(M) where M is the matrix).
Henrik
On 11 June 2010 21:57, Dave Roberts 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,
>
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 Al
Le 11/06/2010 12:45, Henrik Aldberg a écrit :
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 ro
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 time
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