CLARA and determining the right number of clusters
Hi there,
I've tried your suggestion and it seems promising. But I have a couple of questions. I am reading a three column ASCII file (lon, lat, sst)
mydata <- read.table("INFILE", header=FALSE,sep="",
na.strings="99.00",dec=".",strip.white=TRUE,col.names=c("lon","lat","sst"))
then I extract a subset of the data and try to get the right number of
clusters just for third var, sst
I'm not sure whether you feel that you have to look at a single variable at a time, but the whole thing should work for more than one as well.
x<-mydata$sst asw <- numeric(10) for (k in 4:10)
+ asw[k] <- clara(x, k) $ silinfo $ avg.width
k.best <- which.max(asw)
cat("silhouette-optimal number of clusters:", k.best, "\n")
silhouette-optimal number of clusters: 5 I've changed the maximum number of clusters in your example from 20 just to 10 as I am expecting a number between 5 and 8 clusters would be right. Is there any problem with this change? Maybe this restriction is too strict if I just consider the data are just numbers but as it is sea surface temperature under certain "environmental-meteorological conditions" in this particular case I think there should not be more than 8-9 clusters (If 20 is retained I get 11 clusters).
This kind of problem concerns your data and your application and cannot be solved on such a mailing list. Perhaps you should go for professional advice about your particular data. Quite obviously, if you restrict the number of clusters to be at most 10, you cannot find eleven, and how strong you "think that there should not be more than 8-9 clusters" and how good your arguments against 11 are, nobody on this list can decide. The general problem is that there is no unique statistical definition of what a "true cluster" is and whether your dataset rather contains 5 or 11 clusters (or any other number) depends on what you want to call a "cluster".
The second question is how should one understand the plot? Is the right number the one with greater "average silhouette width"?
I don't know which plot you refer to but you may have a look at the Kaufman and Rousseeuw book quoted on the help page. Best wishes, Christian
Thanks again 2008/9/30 Christian Hennig <chrish at stats.ucl.ac.uk>
Hi there, generally finding the right number of clusters is a difficult problem and depends heavily on the cluster concept needed for the particular application. No outcome of any automatic mathod should be taken for granted. Having said that, I guess that something like the example given in
?pam.object
(replacing pam by clara) should work with clara, too. Regards, Christian On Tue, 30 Sep 2008, pacomet wrote: Hi everyone
I have a question about clustering. I've managed using CLARA to get a clustering analysis of a large data set. But now I want to find which is the right number of clusters. The clara.object gives some information like the ratio between maximal and minimal dissimilarity that says (maybe if lower than 1??) if a cluster is well-separated from the other. I've also read something about silhouette and abut cluster.stats but can't manage to get how to find the right number of clusters. I've tried a suggestion from the mailing list but when using dist d1<-dist(mydata$sst) it says that "specified vector size is too big" Is there any method to find the right number of clusters when using clara? Maybe something I've tried but with a small and simple trick I can't find Thanks in advance --
_________________________ El ponent la mou, el llevant la plou Usuari Linux registrat: 363952 ------- Fotos: http://picasaweb.google.es/pacomet [[alternative HTML version deleted]] ______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
*** --- *** Christian Hennig University College London, Department of Statistical Science Gower St., London WC1E 6BT, phone +44 207 679 1698 chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche<http://www.homepages.ucl.ac.uk/%7Eucakche>
--
_________________________ El ponent la mou, el llevant la plou Usuari Linux registrat: 363952 ------- Fotos: http://picasaweb.google.es/pacomet
*** --- *** Christian Hennig University College London, Department of Statistical Science Gower St., London WC1E 6BT, phone +44 207 679 1698 chrish at stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche