How to extract cluster centres from agnes for inputting into kmeans?

Question

One recommended method for getting a good cluster solution is to first use a hierarchical clustering method, choose a number of clusters, then extract the centroids, and then rerun it as a K-means clustering algorithm with the centres pre-specified. A toy example:

library(cluster)
data(animals)
ag.a  <- agnes(agriculture, method = "ward")
ag.2 <- cutree(ag.a, k = 2)

This would give me two clusters. How can I extract the cluster centres in a format that I can then put into the kmeans() algorithm and reapply it to the same data?

Answer 1

You can use the clustering to assign cluster membership and then calculate the center for all the observations in a cluster. The kmeans function allows you to specify initial centers via the centers= parameter if you pass in a matrix. You can do that with

library(cluster)
data(animals)
ag.a  <- agnes(agriculture, method = "ward")
ag.2 <- cutree(ag.a, k = 2)

# calculate means per group
cent<-aggregate(cbind(x,y)~ag.2, agriculture, mean)
# pass as initial centers
kmeans(agriculture, cent[,-1])