[R] Document clustering for R

Christian Hennig chrish at stats.ucl.ac.uk
Tue Sep 13 11:05:31 CEST 2005

If you are able to implement the computation of the distance matrix, you
can use methods such as pam, agnes and hclust, which operate on
dissimilarity matrices of any kind. You may also perform a
multidimensional scaling with isoMDS, sammon or cmdscale and use any
clustering algorithm for n*p data on the outcome.


On Mon, 12 Sep 2005, Raymond K Pon wrote:

> I'm working on a project related to document clustering. I know that R
> has clustering algorithms such as clara, but only supports two distance
> metrics: euclidian and manhattan, which are not very useful for
> clustering documents. I was wondering how easy it would be to extend the
> clustering package in R to support other distance metrics, such as
> cosine distance, or if there was an API for custom distance metrics.
> Best regards,
> Raymond Pon
> pon3 at llnl.gov
> x43062
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche

More information about the R-help mailing list