Hello Christian
Thanks for answering. Yes, I have tried dbscan from fpc but I'm still
stuck on the memory problem. Regarding your answer, I'm not sure which
memory parameter should I look at. Following is the code I tried with
dbscan parameters, maybe you can see if there is any mistake.
sstdat=read.csv("sst.dat",sep=";",header=F,col.names=c("lon","lat","sst"))
library(fpc)
sst1=subset(sstdat, sst<50)
sst2=subset(sst1, lon>-6)
sst2=subset(sst2, lon<40)
sst2=subset(sst2, lat<46)
> dbscan(sst2$sst, 0.1, MinPts = 5, scale = FALSE, method =
c("hybrid"), seeds = FALSE, showplot = FALSE, countmode = NULL)
Error: no se puede ubicar un vector de tamaño 858.2 Mb
> head(sst2)
lon lat sst
1257 35.18 24.98 26.78
1258 35.22 24.98 26.78
1259 35.27 24.98 26.78
1260 35.31 24.98 26.78
1261 35.35 24.98 26.78
1262 35.40 24.98 26.85
In this example I only apply dbscan to temperature values, not lon/lat,
so eps parameter is 0.1. As it is a gridded data set any point is
surrounded by eight data points, then I thought that at least 5 of the
surrounding points should be within the reachability distance. But I'm
not sure I'm getting the right approach by only considering temperature
value, maybe then I'm missing spatial information. How should I deal
with longitude and latitude data?
dimensions of sst2 are: 152243 rows x 3 columns
Thanks again
El 03/06/2011 18:24, Christian Hennig escribió:
Have you considered the dbscan function in library fpc, or was it
another one?
dbscan in fpc doesn't have a "distance" parameter but several options,
one
of which may resolve your memory problem (look up the documentation of
the "memory" parameter).
Using a distance matrix for hundreds of thousands of points is a
recipe for disaster (memory-wise). I'm not sure whether the function
that you used did that, but dbscan in fpc can avoid it.
It is true that dbscan requires tuning constants that the user has to
provide. There is unfortunately no general rule how to do this; it
would be necessary to understand the method and the meaning of the
constants, and how this translates into the requirements of your
application.
You may try several different choices and do some cluster validation
to see what works, but I can't explain this in general terms easily
via email.
Hope this helps at least a bit.
Best regards,
Christian
On Fri, 3 Jun 2011, Paco Pastor wrote:
Hello everyone,
When looking for information about clustering of spatial data in R I
was directed towards DBSCAN. I've read some docs about it and theb
new questions have arisen.
DBSCAN requires some parameters, one of them is "distance". As my
data are three dimensional, longitude, latitude and temperature,
which "distance" should I use? which dimension is related to that
distance? I suposse it should be temperature. How do I find such
minimum distance with R?
Another parameter is the minimum number of points neded to form a
cluster. Is there any method to find that number? Unfortunately I
haven't found.
Searching thorugh Google I could not find an R example for using
dbscan in a dataset similar to mine, do you know any website with
such kind of examples? So I can read and try to adapt to my case.
The last question is that my first R attempt with DBSCAN (without a
proper answer to the prior questions) resulted in a memory problem. R
says it can not allocate vector. I start with a 4 km spaced grid with
779191 points that ends in approximately 300000 rows x 3 columns
(latitude, longitude and temperature) when removing not valid SST
points. Any hint to address this memory problem. Does it depend on my
computer or in DBSCAN itself?
Thanks for the patience to read a long and probably boring message
and for your help.
--
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15/1999, usted puede ejercitar sus derechos de acceso, rectificación,
cancelación y, en su caso, oposición enviando una solicitud por escrito,
acompañada de una fotocopia de su DNI dirigida a: Fundación de la Comunidad
Valenciana Centro de Estudios Ambientales del Mediterráneo - CEAM. C/ Charles
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