Laboratorio Capitulo 10.5

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

##  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

set.seed(4)
km.out=kmeans (x,3, nstart =20)
km.out

## K-means clustering with 3 clusters of sizes 10, 23, 17
## 
## Cluster means:
##         [,1]        [,2]
## 1  2.3001545 -2.69622023
## 2 -0.3820397 -0.08740753
## 3  3.7789567 -4.56200798
## 
## Clustering vector:
##  [1] 3 1 3 1 3 3 3 1 3 1 3 1 3 1 3 1 3 3 3 3 3 1 3 3 3 2 2 2 2 2 2 2 2 2 2
## [36] 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2
## 
## Within cluster sum of squares by cluster:
## [1] 19.56137 52.67700 25.74089
##  (between_SS / total_SS =  79.3 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"    
## [5] "tot.withinss" "betweenss"    "size"         "iter"        
## [9] "ifault"

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

## [1] 104.3319

km.out=kmeans (x,3, nstart =20)
km.out$tot.withinss

## [1] 97.97927

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)

cutree(hc.complete , 2)

##  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
## [36] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

cutree(hc.average , 2)

##  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 2 2
## [36] 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2

cutree(hc.single , 2)

##  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

cutree(hc.single , 4)

##  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3
## [36] 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3

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 ="")