Dave,

I used daisy with the default settings (daisy(M) where M is the matrix).


Henrik

On 11 June 2010 21:57, Dave Roberts <dvrbts@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]]
>>
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>>
>
> -
>

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