Are there R packages that allow for dynamic clustering, i.e. where the number of clusters are not predefined? I have a list of numbers that falls in either 2 or just 1 cluster. Here an example of one that should be clustered into two clusters:
two <- c(1,2,3,2,3,1,2,3,400,300,400) and here one that only contains one cluster and would therefore not need to be clustered at all. one <- c(400,402,405, 401,410,415, 407,412) Given a sufficiently large amount of data, a statistical test or an effect size should be able to determined if a data set makes sense to be divided i.e. if there are two groups that differ well enough. I am not familiar with the underlying techniques in kmeans, but I know that it blindly divides both data sets based on the predefined number of clusters. Are there any more sophisticated methods that allow me to determine the number of clusters in a data set based on statistical tests or effect sizes ? Is it possible that this is not a clustering problem but a classification problem? Ralf ______________________________________________ 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.
