[R] k-means with euclidian distance but no coordinates

[Prof Brian Ripley]

On Fri, 14 Dec 2001, Corrin Lakeland wrote:

> Hi,
>
> I'm trying to build a thesaurus that will sensible values for rare words.
> I suspect the best algorithm to use is k-means although I'm not sure about
> that -- I would have preferred a k dimensional space with a binary cluster
> in each dimension so a word can belong to 0..k clusters, but I digress...
>
> I can measure the strength of correlation between words fairly easily by
> counting cooccurance divided by frequency of each word, giving a euclidian
> distance, although this doesn't work especially well for rare words.
> However I don't have coordinates as such, and deriving them given distance
> is non-trivial.
>
> Now, as I understand k-means, it uses euclidian distance rather than
> coordiantes, the first step given in texts is to derive the distance given
> the coordinates. But I can't find a way to call the built in function
> without coordinates.  I had a look at R-1.3.1/src/library/mva/src/kmns.f
> but my Fortran isn't good and I had enough trouble following the code, so
> I'm not up to making major changes.

By definition K-means needs coordinates!  Try pam/clara in library
cluster.

I think you have a distance, but not a Euclidean (sic) one.  If you did
have a Euclidean distance, cmdscale would (easily) give you coordinates.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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