Chapter 10 Lab 2 Clustering.R

# Chapter 10 Lab 2: Clustering
library(ISLR)
# K-Means Clustering

set.seed(2)
x <- matrix(rnorm(50*2), ncol=2)
x[1:25,1] <- x[1:25,1]+3
x[1:25,2] <- x[1:25,2]-4
km.out <- kmeans(x,2,nstart=20)
km.out$cluster
plot(x, col=(km.out$cluster+1), main="K-Means Clustering Results with K=2", 
     xlab="", ylab="", pch=20, cex=2)

set.seed(4)
km.out <- kmeans(x,3,nstart=20)
km.out
plot(x, col=(km.out$cluster+1), main="K-Means Clustering Results with K=3", 
     xlab="", ylab="", pch=20, cex=2)

set.seed(3)
km.out <- kmeans(x,3,nstart=1)
km.out$tot.withinss
km.out <- kmeans(x,3,nstart=20)
km.out$tot.withinss

# Hierarchical Clustering

hc.complete <- hclust(dist(x), method="complete")
hc.average <- hclust(dist(x), method="average")
hc.single <- hclust(dist(x), method="single")
par(mfrow=c(1,3))
plot(hc.complete,main="Complete Linkage", xlab="", sub="", cex=.9)
plot(hc.average, main="Average Linkage", xlab="", sub="", cex=.9)
plot(hc.single, main="Single Linkage", xlab="", sub="", cex=.9)
dev.off()
# prune the trees
cutree(hc.complete, 2)
cutree(hc.average, 2)
cutree(hc.single, 2)
cutree(hc.single, 4)
xsc <- scale(x)
plot(hclust(dist(xsc), method="complete"), main="Hierarchical Clustering with Scaled Features")
x <- matrix(rnorm(30*3), ncol=3)
dd <- as.dist(1-cor(t(x)))
plot(hclust(dd, method="complete"), 
     main="Complete Linkage with Correlation-Based Distance", xlab="", sub="")