This should get you started
> set.seed(42)
> x <- matrix(rnorm(200, 25, 5), 40, 5)
> x.clus <- hclust(dist(x))
> x.g4 <- cutree(x.clus, 4)
> x.cent <- aggregate(x, list(x.g4), mean)
> x.km <- kmeans(x, x.cent[,-1])
> xtabs(~x.g4+x.km$cluster)
x.km$cluster
x.g4 1 2 3 4
1 10 0 1 0
2 0 12 2 0
3 0 2 10 0
4 0 0 0 3
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of marioger
Sent: Friday, May 16, 2014 7:29 AM
To: r-help@r-project.org
Subject: [R] Using centers of hierarchical clustering for k-means
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
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