R: Cluster analysis with hclust(). How to get the cluster representatives?

Question

I am doing some cluster analysis with R. I am using the hclust() function and I would like to get, after I perform the cluster analysis, the cluster representative of each cluster.

I define a cluster representative as the instances which are closest to the centroid of the cluster.

So the steps are:

Finding the centroid of the clusters
Finding the cluster representatives

I have already asked a similar question but using K-means: https://stats.stackexchange.com/questions/251987/cluster-analysis-with-k-means-how-to-get-the-cluster-representatives

The problem, in this case, is that hclust doesn't give the centroids!

For example, saying that d are my data, what I have done so far is:

hclust.fit1 <- hclust(d, method="single")     
groups1 <- cutree(hclust.fit1, k=3) # cut tree into 3 clusters

## getting centroids ##

mycentroid <- colMeans(CV)    
clust.centroid = function(i, dat, groups1) {    
  ind = (groups1 == i)   
  colMeans(dat[ind,])
}

centroids <- sapply(unique(groups1), clust.centroid, data, groups1)

But now, I was trying to get the cluster representatives with this code (I got it in the other question I asked, for k-means):

index <- c()

for (i in 1:3){    
  rowsum <- rowSums(abs(CV[which(centroids==i),1:3] - centroids[i,]))    
  index[i] <- as.numeric(names(which.min(rowsum)))   
}

And it says that:

"Error in e2[[j]] : index out of the limit"

I would be grateful if any of you could give me a little help. Thanks.

-- (not) Working example of the code --

example_data.txt

A,B,C
10.761719,5.452188,7.575762
10.830457,5.158822,7.661588
10.75391,5.500170,7.740330
10.686719,5.286823,7.748297
10.864527,4.883244,7.628730
10.701415,5.345650,7.576218
10.820583,5.151544,7.707404
10.877528,4.786888,7.858234
10.712337,4.744053,7.796390

As for the code:

# Install R packages

#install.packages("fpc")

#install.packages("cluster")

#install.packages("rgl")

library(fpc)
library(cluster)
library(rgl)

CV <- read.csv("example_data")

str(CV)

data <- scale(CV)

d <- dist(data,method = "euclidean")
hclust.fit1 <- hclust(d, method="single") 
groups1 <- cutree(hclust.fit1, k=3) # cut tree into 3 clusters
mycentroid <- colMeans(CV)

clust.centroid = function(i, dat, groups1) {
  ind = (groups1 == i)
  colMeans(dat[ind,])
}

centroids <- sapply(unique(groups1), clust.centroid, CV, groups1)

index <- c()
for (i in 1:3){
  rowsum <- rowSums(abs(CV[which(centroids==i),1:3] - centroids[i,]))
  index[i] <- as.numeric(names(which.min(rowsum)))
}

Please see stackoverflow.com/questions/5963269/… on how to make a reproducible example. Right now we can't run your code start to finish without a lot of guess work. — emilliman5

Has QUIT--Anony-Mousse Has QUIT--Anony-Mousse · Accepted Answer · 2016-12-31T15:30:19

Hierarchical clustering does not use (or compute) representatives.

In particular for single link (but it can also happen for other linkages), the "center" can be in a different cluster. Just consider the top two data sets in example:

enter link description here

Furthermore, the centroid (mean) is connected to Euclidean distance. With other distances, it may be a very bad representative.

So use with care!

Either way, hierarchical clustering does not define or compute a representative. You will have to do this yourself.

R: Cluster analysis with hclust(). How to get the cluster representatives?

1 Answers