You might want to check if there is a neural gas algorithm in R. kmeans generally has a high variance since it is very dependent on the initialization. Neural gas overcomes this problem by using a ranked list of neighbouring data points instead using data points directly. It is more stable (at the cost of additional computational time).
On 25.09.2007, at 05:00, Julia Kröpfl wrote: > I applied kmeans to my data: > > kcluster= kmeans((mydata, 4, iter.max=10) > table(code, kcluster$cluster) > > If I run this code again, I get a different result as with the > first trial (I understand that this is correct, since kmeans starts > randomly with assigning the clusters and therefore the outcomes can > be different) > But is there a way to stabilize the cluster (meaning finding the > one cluster that appears the most often in 10 trials)? > > Thank you for any ideas, > Julia > -- > > ______________________________________________ > R-help@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. ______________________________________________ R-help@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.
