I am running a hierarchical clustering process in R
, using daisy
to compute a dissimilarity matrix and agnes
for hierarchical clustering, as described in Clustering of mixed type data with R.
With my 8 GB Ram, I constantly run into this error:
Error: cannot allocate vector of size 1.8 Gb
I have 21836 rows with only 2 variables. However, I'd like to use more variables, but I am already running out of memory using only 2.
Are there any alternative algorithms for a mixed data set of continuous and categorical variables?
Are there any alternative tools (I am currently using R) which would require less memory?
21836^2
. Multiply by 8 and you'll get roughtly 4 Gb RAM needed only to store the input matrix. But the procedure (and computer) surely needs more free memory to be able to perform. I'm not R user, though, so please wait for somebody knowing R well to advise you. $\endgroup$