[R] Jaccard dissimilarity matrix for PCA

[Christian Hennig]

jaccard in package prabclus computes a Jaccard matrix for you.

By the way, if you want to do hierarchical clustering, it doesn't seem to 
be a good idea to me to run PCA first. Why 
not cluster the dissimilarity matrix directly without information loss by 
PCA? (I should not make too general statements on this because generally 
how to cluster data always depends on the aim of clustering, the cluster 
concept you are interested in etc.)

prabclus also contains clustering methods for such data; have a 
look at the functions prabclust and hprabclust (however, they are 
documented as functions for clustering species distribution ranges, so if 
your application is different, you may have to think about whether and how 
to adapt them).

Hope this helps,
Christian




On Tue, 28 Dec 2010, Flabbergaster wrote:

>
> Hi
> I have a large dataset, containing a wide range of binary variables.
> I would like first of all to compute a jaccard matrix, then do a PCA on this
> matrix, so that I finally can do a hierarchical clustering on the principal
> components.
> My problem is, that I don't know how to compute the jaccard dissimilarity
> matrix in R? Which package to use, and so on...
> Can anybody help me?
> Alternatively I'm search for another way to explore the clusters present in
> my data.
> Another problem is, that I have cases with missing values on different
> variables.
>
> Jacob
> --
> View this message in context: http://r.789695.n4.nabble.com/Jaccard-dissimilarity-matrix-for-PCA-tp3165982p3165982.html
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>
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*** --- ***
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