[R-sig-eco] Clustering large data

[Peter Solymos]

Dear Thierry,

the 'mefa' package should do this, and I am also interested in the
testing of the package for such a large number of species. I have used
it before with 75K records, but only with ~160 species and 1052 sites.
So please let me know if it worked!

You can do the clustering like this (SAMPLES and SPECIES are the two
column in the long format, have to be the same length):

x <- mefa(stcs(data.frame(SAMPLES,SPECIES)))
cl <- hclust(dist(x$xtab))

Hope this works,

Peter

Peter Solymos, PhD
Department of Mathematical and Statistical Sciences
University of Alberta
Edmonton, Alberta, T6G 2G1
CANADA



On Tue, Oct 7, 2008 at 4:12 AM, ONKELINX, Thierry
<Thierry.ONKELINX at inbo.be> wrote:
> Dear all,
>
> We have a problem with a large dataset that we want to cluster. The
> dataset is in a long format: 1154024 rows with presence data. Each row
> has the name of the species and the location. We have 1381 species and
> 6354 locations.
> The main problem is that we need the data in wide format (one row for
> each location, one column for each species) for the clustering
> algorithms. But the 6354 x 1381 dataframe is too big to fit into the
> memory. At least when we use cast from the reshape package to convert
> the dataframe from a long to a wide format.
>
> Are there any clustering tools available that can work with the data in
> a long format or with sparse matrices (only 13% of the matrix is
> non-zero)? If the work with sparse matrices: how to convert our dataset
> to a sparse matrix? Other suggestions are welcome.
>
> We are working with R 2.7.2 on WinXP with 2 GB RAM. --max-mem-size is
> set to 2047M.
>
> Thanks,
>
> Thierry
>
>
> ------------------------------------------------------------------------
> ----
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and Forest
> Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
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> Thierry.Onkelinx at inbo.be
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