The easy way is to use a machine with say 32 Gb of ram. You can rent them
by the hour from AWS or google cloud at very reasonable prices.
Best,
Ista
On Nov 27, 2015 8:39 AM, "Ajay Ramaseshan" <ajay_ramases...@hotmail.com>
wrote:
> Hello,
>
>
> I am trying the DBSCAN clustering algorithm on a huge data matrix (26000 x
> 26000). I dont have the datapoints, just the distance matrix. It comes to
> 17 GB in the hard disk, and needs to be loaded into R to use the DBSCAN
> implementation (under package fpc). So I tried using read.csv but R crashed.
>
>
> I am getting the message 'Killed after it runs for 10 minutes'
>
>
> dist<-read.csv('dist.csv',header=FALSE)
> Killed
>
> So I chceked is there any R package that handles big data like this, and
> came across bigmemory package in R. So I installed it and ran this command,
> but even this does not work, R exits.
>
> > dist<-read.big.matrix('dist.csv',sep=',',header=FALSE)
>
> *** caught bus error ***
> address 0x7fbc4faba000, cause 'non-existent physical address'
>
> Traceback:
> 1: .Call("bigmemory_CreateSharedMatrix", PACKAGE = "bigmemory", row,
> col, colnames, rownames, typeLength, ini, separated)
> 2: CreateSharedMatrix(as.double(nrow), as.double(ncol),
> as.character(colnames), as.character(rownames), as.integer(typeVal),
> as.double(init), as.logical(separated))
> 3: big.matrix(nrow = numRows, ncol = createCols, type = type, dimnames =
> list(rowNames, colNames), init = NULL, separated = separated,
> backingfile = backingfile, backingpath = backingpath, descriptorfile =
> descriptorfile, binarydescriptor = binarydescriptor, shared = TRUE)
> 4: read.big.matrix("dist.csv", sep = ",", header = FALSE)
> 5: read.big.matrix("dist.csv", sep = ",", header = FALSE)
>
> Possible actions:
> 1: abort (with core dump, if enabled)
> 2: normal R exit
> 3: exit R without saving workspace
> 4: exit R saving workspace
> Selection: 2
> Save workspace image? [y/n/c]: n
> Warning message:
> In read.big.matrix("dist.csv", sep = ",", header = FALSE) :
> Because type was not specified, we chose double based on the first line
> of data.
>
>
> So how do I handle such huge data in R for DBSCAN? Or is there any other
> implementation of DBSCAN in other programming language which can handle
> such a huge distance matrix of 17 GB ?
>
>
>
> Regards,
>
> Ajay
>
> [[alternative HTML version deleted]]
>
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>
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