If I understand you correctly and each factor consists of binary data, you
may want to check out monothethic analysis, available in the package
'cluster'.

For a simple example and short description of the method to get you started,
just type in:

require(cluster)
?mona

As far as i know there's nothing strictly theoretically invalid in using
hierarchical clustering on binary data, and you may want to see how results
may differ by trying the example data (and your own data) with other
clustering methods. For example, compare:

require(cluster)
data(animals)

plot(mona(animals))
plot(agnes(animals))


2010/9/2 tueken <hannat_tueken@hotmail.com>

>
> Hello all
>
> I wonder what can i use to cluster vectors which composed of several
> factors.
> lets say around 30 different factors compose a vector, and if the factor is
> present then it encoded as 1, if not presented then it will be encoded as
> 0.
> I was thinking of using hierarchical clustering, as i know the distance
> between two vector were calculated through euclidean distance function, but
> i dont think this distance would be correct to separate the data, cause two
> vector with different composition, could end up having similar distance to
> another vector.
> hope someone could give me some clue!
> --
> View this message in context:
> http://r.789695.n4.nabble.com/how-to-cluster-vectors-of-factors-tp2514654p2514654.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.
>

	[[alternative HTML version deleted]]

