That kmeans returns an error if there is an empty cluster is a bit of a
nuisance.
It should not be too difficult to get rid off the kmeans function for what
you call "reclustering". You could write your own function that assigns
every point of the new data to the closest initial center. That should be
relatively easy and does the same thing, if I understand correctly what you
want.
I don't comment on whether it makes sense what you attempt to do, which
entirely depends on the aim of your analysis (and on what you mean by
"cluster in the same way"), but an alternative could be
to cluster the initial data by mclustBIC in library mclust and to use the
resulting clusters as training data in mclustDA.
Cheers,
Christian
On Mon, 11 Jan 2010, t.peter.muel...@gmx.net wrote:
K-means recluster data with given cluster centers
Dear R user,
I have several large data sets. Over time additional new data sets will be
created.
I want to cluster all the data in a similar/ identical way with the k-means
algorithm.
With the first data set I will find my cluster centers and save the cluster
centers to a file [1].
This first data set is huge, it is guarantied that cluster centers will
converge.
Afterwards I load my cluster centers and cluster via k-means all other datasets
with the same cluster centers [2].
I tried this but now I'm getting in the reclustering step following error
message:
"Error: empty cluster: try a better set of initial centers"
That one of the clusters is empty (has no datapoint) should not be a
problem. This can happen because the new data sets can be smaller. What
am I doing wrong? Is there a other way to cluster new data in the same
way like the old datasets?
Thanks
Peter
1: R code to find cluster center and save them to file
#---INITIAL CLUSTERING TO FIND CLUSTER CENTERS
# LOAD LIB
library(cluster)
# LOAD DATA
data_unclean <- read.table("dataset1.dat")
data.matrix<-as.matrix(data_unclean,"any")
# CLUSTER
Nclust <- 100 # amount cluster centers
Imax <- 200 # amount of iteration for convergence of clustering
set.seed(100) # set seed of random nr generator
init <- sample(dim(data.matrix)[1], Nclust) # this is the initial Nclust
prototypes
km <- kmeans(data.matrix, centers=data.matrix[init,], iter.max=Imax)
# WRITE OUT CLUSTER CENTERS
km$centers # print cluster center (columns: dim component; rows: clusters)
km$size # print amount of data in each cluster
clusterCenters=km$centers
save(file="clusterCenters.RData", list='clusterCenters') # Beispiel
write.table(km$centers, file = "clusterCenters.dat", sep = ",", col.names=
FALSE, row.names= FALSE)
2: R code to recluster new data
#---RECLUSTER NEW DATA WITH GIVEN CLUSTER CENTERS
# LOAD LIB, SET PARAMETER
library(cluster)
loopStart="0"
loopEnd="10"
# LOAD CLUSTER CENTER
load("clusterCenters.RData") # load cluster centers
# LOOP OVER TRAJ AND RECLUSTER THEM
for(ii in loopStart:loopEnd){
# DEFINE FILENAME
#print(paste("test",ii,sep=""))
filenameInput=paste("dataset",ii,"dat",sep="")
filenameOutput=paste("dataset",ii,"datClusters",sep="")
print(filenameInput)
print(filenameOutput)
# LOAD DATA
data_unclean <- read.table(filenameInput)
data.matrix<-as.matrix(data_unclean,"any")
# RECLUSTER DATA
kmRecluster <- kmeans(data.matrix, centers=clusterCenters, iter.max=1)
kmRecluster$size
# WRITE OUT CLUSTERS FOR EACH DATA
write.table(kmRecluster$cluster, file = filenameOutput, sep = ",",
col.names= FALSE, row.names= FALSE)
}
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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chr...@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
