[R] Jaccard dissimilarity matrix for PCA
chrish at stats.ucl.ac.uk
Tue Dec 28 16:57:08 CET 2010
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,
On Tue, 28 Dec 2010, Flabbergaster wrote:
> 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
> 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
> View this message in context: http://r.789695.n4.nabble.com/Jaccard-dissimilarity-matrix-for-PCA-tp3165982p3165982.html
> Sent from the R help mailing list archive at Nabble.com.
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> and provide commented, minimal, self-contained, reproducible code.
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