Hi Abanero,

first, I have to correct myself. Knn1 is a supervised learning algorithm, so
my comment wasn't completely correct. In any case, if you want to do a
clustering prior to a supervised classification, the function daisy() can
handle any kind of variable. The resulting distance matrix can be used with
a number of different methods.

And you're right, randomForest doesn't handle categorical variables either.
So I haven't been of great help here...
Cheers
Joris

On Thu, May 27, 2010 at 1:25 PM, abanero <gdevitis@xtel.it> wrote:

>
> Hi,
>
> thank you Joris and Ulrich for you answers.
>
> Joris Meys wrote:
>
> >see the library randomForest for example
>
>
> I'm trying to find some example in randomForest with categorical variables
> but I haven't found anything. Do you know any example with both categorical
> and numerical variables? Anyway I don't have any class labels yet. How
> could
> I  find clusters with randomForest?
>
>
> Ulrich wrote:
>
> >Probably the simplest way is Affinity Propagation[...] All you need is a
> way of measuring the similarity of >samples which is straightforward both
> for numerical and categorical variables.
>
> I had a look at the documentation of the package apcluster. That's
> interesting but do you have any example using it with both categorical and
> numerical variables? I'd like to test it with a large dataset..
>
> Thanks a lot!
> Cheers
>
> Giuseppe
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/cluster-analysis-and-supervised-classification-an-alternative-to-knn1-tp2231656p2232950.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> 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.
>



-- 
Joris Meys
Statistical Consultant

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

Coupure Links 653
B-9000 Gent

tel : +32 9 264 59 87
Joris.Meys@Ugent.be
-------------------------------
Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php

	[[alternative HTML version deleted]]

