Well, you could use the group membership from the clustering along with, for instance, aggregate() to get the mean values for each cluster, and pass those to kmeans() using the centers argument as described in the help file.
Unless you want medoids instead of centroids, since you didn't specify? If you post a reproducible example including the code for the parts you know how to do, you might get someone willing to fill in the code for the unknown parts. Sarah On Fri, May 16, 2014 at 8:29 AM, marioger <mario_wieg...@gmx.de> wrote: > Hi, > > i have the following problem: I am using k-means algorithm for clustering. > But instead of using randomized centers, I would like to use centers created > by hierarchical clustering. So I want to apply "hclust" on my data set (in > this case the iris data), getting a solution by "cutree", calculating the > means/centers of the resulting clusters and use these centers as starting > points for k-means clusterng. But I have no idea how I calculate the centers > of the clusters and how to use them as starting points for the k-means > algorithm. > > Hope you can help. Thanks in advance. > > Mario > > -- Sarah Goslee http://www.functionaldiversity.org ______________________________________________ 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.
