Hi,

The output of the provided example shows you which kind of matrix you need for 
cluster centers. In the example, 3 centers per variable (4) are defined. Thus, 
the initial cluster centers are defined by a 3x4 matrix.

HTH,
Pascal



________________________________
 De : Ahmed Elgohary <aagoh...@gmail.com>
À : r-help@r-project.org 
Cc : Pascal Oettli <kri...@ymail.com> 
Envoyé le : Mercredi 3 avril 2013 22h53
Objet : Re: [R] kernlab::kkmeans initial centers


I am not asking about k-means. I am asking about passing initial assignments to 
the kernel k means algorithm. In kernel k-means, centroids are not defined 
explicitly. I tried passing initial centroids in the original feature space 
though. But, it did not work. The provided example just sets the number of 
clusters and lets the method assigns random cluster memberships. I am also 
wondering about the output centers. Are they the centers in the eigenvectors 
space? If so, what kind of initial centers I am supposed to pass to the method?

--ahmed


On Wed, Apr 3, 2013 at 12:54 AM, Pascal Oettli <kri...@ymail.com> wrote:

Hi,
>
>I would say that if you know what k-means algorithm is, you know the meaning 
>of "initial cluster centers".
>
>You can also check the output of the provided example.
>
>Regards,
>Pascal
>
>
>On 04/03/2013 09:27 AM, Ahmed Elgohary wrote:
>
>Hi,
>>
>>I am trying to pass initial cluster assignments to the kkmeans
>>
method<http://rss.acs.unt.edu/Rdoc/library/kernlab/html/kkmeans.html>of
>>
>>kernlab. It is not clear to me how I can set the parameter
>>
*centers* with "initial cluster centers" as stated in the documentation?
>>
>>thanks,
>>
>>--ahmed
>>
>>        [[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.
>>
>>
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