How to produce a pretty plot of the results of k-means cluster analysis?

Question

I'm using R to do K-means clustering. I'm using 14 variables to run K-means

• What is a pretty way to plot the results of K-means?
• Are there any existing implementations?
• Does having 14 variables complicate plotting the results?

I found something called GGcluster which looks cool but it is still in development. I also read something about sammon mapping, but didn't understand it very well. Would this be a good option?

Answer 1

I'd push the silhouette plot for this, because it's unlikely that you'll get much actionable information from pair plots when the number of dimension is 14.

library(cluster)
library(HSAUR)
data(pottery)
km <- kmeans(pottery,3)
dissE <- daisy(pottery) 
dE2 <- dissE^2
sk2 <- silhouette(km$cl, dE2)
plot(sk2)

This approach is highly cited and well known (see here for an explanation).

Rousseeuw, P.J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53-65.

Answer 2

Here an example that can helps you:

library(cluster)
library(fpc)

data(iris)
dat <- iris[, -5] # without known classification 
# Kmeans clustre analysis
clus <- kmeans(dat, centers=3)

# Fig 01
plotcluster(dat, clus$cluster)

# More complex
clusplot(dat, clus$cluster, color=TRUE, shade=TRUE, 
         labels=2, lines=0)

# Fig 03
with(iris, pairs(dat, col=c(1:3)[clus$cluster])) 

Based on the latter plot you could decide which of your initial variables to plot. Maybe 14 variables are huge, so you can try a principal component analysis (PCA) before and then use the first two or three components from the PCA to perform the cluster analysis.