On Tue, 17 Nov 2009, akonla wrote:
Hi,
I am new to clustering in R and I have a dataset with approximately 17,000
rows and 8 columns with each data point a numerical character with three
decimal places. I would like to cluster the 8 columns so that I get a
dendrogram as an output. So, I am simply creating a distance matrix of my
data, using the 'hclust' function, and then plotting the results (see below,
my data is contained in the text file).
x<-read.table('SEP_IR_1113_3.txt', header=TRUE,sep="\t')
x.dist=dist(x)
See
?dist
which explains
This function computes and returns the distance matrix computed by
using the specified distance measure to compute the distances
between the rows of a data matrix.
You are trying to cluster 17,000 rows.
No wonder it (dist) is taking its time!
Chuck
hc=hclust(x.dist,method="average")
plot(hc, hang=-1)
Unfortunately, the hclust function, although it produces no error terms,
takes a very long time to run (>4 hours) and my computer kills the program
before it finishes. I don't think this data set is so large to cause such a
long computing time, and I have plenty of memory since I am running this
analysis on our university computing cluster.
Has anyone run into this problem before and does anyone have any tips on how
I can speed up processing? I can provide extra information if necessary
regarding my problem.
Thank you!
--
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