library(MASS)
library(ElemStatLearn)
data(nci)

dim(nci)
head(nci)

X <- t(nci)

D <- dist(X, method = "euclidean")

n <- dim(X)[1]
full <- matrix(0, n, n)
full[lower.tri(full)] <- D
D <- full + t(full)

names <- colnames(nci)
mds.obj <- cmdscale(D,k=2)  
eqscplot(mds.obj,type="n")
text(mds.obj,labels=names,col=1,cex=0.8)
library(lattice)
xyplot(mds.obj[,2]~mds.obj[,1],groups=names,pch=1:16)
u <- unique(names)
legend("topright",legend=u,col=1:16,pch=1:16)

ClusterMembership <- names

WithinSumsofSquares <- rep(0,20)
for (i in 1:20){
	WithinSumsofSquares[i] <- sum(kmeans(X, centers=i+1,iter=20)$withinss)/64
}
plot(2:21,WithinSumsofSquares)

km.obj <- kmeans(X,centers=3,iter=20)  
ClusterMembership <- km.obj$cluster

xyplot(mds.obj[,2]~mds.obj[,1],groups=ClusterMembership,pch=1:16)
u <- unique(ClusterMembership)
legend("topright",legend=u,col=1:length(u),pch=1:16)
table(names,ClusterMembership)

km.obj <- kmeans(X,centers=10,iter=20)  
ClusterMembership <- km.obj$cluster

xyplot(mds.obj[,2]~mds.obj[,1],groups=ClusterMembership,pch=1:16)
u <- unique(ClusterMembership)
legend("topright",legend=u,col=1:length(u),pch=16)
table(names,ClusterMembership)