Not a direct answer, but from your description it looks like you are better
of with supervised classification algorithms instead of unsupervised
clustering. see the library randomForest for example. Alternatively, you can
try a logistic regression or a multinomial regression approach, but these
are parametric methods and put requirements on the data. randomForest is
completely non-parametric.

Cheers
Joris

On Wed, May 26, 2010 at 3:45 PM, abanero <gdevitis@xtel.it> wrote:

>
> Hi,
> I have a 1.000 observations with 10 attributes (of different types:
> numeric,
> dicotomic, categorical  ecc..) and a measure M.
>
> I need to cluster these observations in order to assign a new observation
> (with the same 10 attributes but not the measure) to a cluster.
>
> I want to calculate for the new observation a measure as the average of the
> meausures M of the observations in the cluster assigned.
>
> I would use cluster analysis ( “Clara” algorithm?) and then “knn1” (in
> package class) to assign the new observation to a cluster.
>
> The problem is: I’m not able to use “knn1” because some of attributes are
> categorical.
>
> Do you know  something like “knn1” that works with categorical variables
> too? Do you have any suggestion?
>
> --
> View this message in context:
> http://r.789695.n4.nabble.com/cluster-analysis-and-supervised-classification-an-alternative-to-knn1-tp2231656p2231656.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> R-help@r-project.org mailing list
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> 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
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