I thought setting keep.data=FALSE might help, but running this on a 32-bit Linux machine, the R process seems to use 1.2 GB until just before clara returns, when it increases to 1.9 GB, regardless of whether keep.data=FALSE or TRUE. Possibly it's the overhead of the .C() interface, but that's mostly an uninformed guess. You could sample your data (say half), remove the original, run clara, keep the mediods, then read your data again and assign each observation to the nearest mediod. This is what clara does anyway, with much smaller samples by default. Reid Huntsinger -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Nestor Fernandez Sent: Wednesday, August 03, 2005 12:45 PM To: r-help at stat.math.ethz.ch Subject: [R] clara - memory limit Dear all, I'm trying to estimate clusters from a very large dataset using clara but the program stops with a memory error. The (very simple) code and the error: mydata<-read.dbf(file="fnorsel_4px.dbf") my.clara.7k<-clara(mydata,k=7)
Error: cannot allocate vector of size 465108 Kb
The dataset contains >3,000,000 rows and 15 columns. I'm using a windows computer with 1.5G RAM; I also tried changing the memory limit to the maximum possible (4000M) Is there a way to calculate clara clusters from such large datasets? Thanks a lot. Nestor.- ______________________________________________ R-help at stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html