# [R] Clustering algorithms don't find obvious clusters

Joris Meys jorismeys at gmail.com
Sun Jun 13 16:07:44 CEST 2010

```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 solution, that is what you want.
Cheers
Joris

On Sat, Jun 12, 2010 at 8:43 PM, Henrik Aldberg
<henrik.aldberg at gmail.com> 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 <dvrbts at 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 at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> -
>>
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> and provide commented, minimal, self-contained, reproducible code.
>

--
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

tel : +32 9 264 59 87
Joris.Meys at Ugent.be
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```