Laboratorio Capítulo 10.5

Alejandra Cermeño

Lab 2: 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

 [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
[28] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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

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
[28] 2 2 2 2 2 2 2 2 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"       
[4] "withinss"     "tot.withinss" "betweenss"   
[7] "size"         "iter"         "ifault"      

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

Hierachical 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)

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
[28] 2 2 2 2 2 2 2 2 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
[28] 2 2 2 2 2 1 2 2 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
[28] 1 1 1 1 1 1 1 1 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
[28] 3 3 3 3 3 3 3 3 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 ="")