set.seed (3)
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.out2 =kmeans (x,2, nstart =20)
km.out2$cluster
plot(x, col =(km.out2$cluster +1) , main="K-Means Clustering Results with K=2", xlab ="", ylab="", pch =20, cex =2)
[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 1 1 1 1 1 1 1 1 1 1 1 1 1 [49] 1 1 [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 1 1 1 1 1 1 1 1 1 1 1 1 1 [49] 1 1 
|
set.seed (4)
km.out3 =kmeans (x,3, nstart =20)
km.out3
plot(x, col =(km.out3$cluster +1) , main="K-Means Clustering
Results with K=3", xlab ="", ylab="", pch =20, cex =2)
K-means clustering with 3 clusters of sizes 13, 25, 12
Cluster means:
[,1] [,2]
1 0.9348942 0.22064634
2 2.7112299 -3.95090815
3 -0.6775392 0.01699628
Clustering vector:
[1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 1 1 3 3 1 1 1 1
3 1 1 3 3 1 1 3 1 3 1 3 3 1
[49] 3 3
Within cluster sum of squares by cluster:
[1] 9.878084 32.323548 10.712040
(between_SS / total_SS = 85.2 %)
Available components:
[1] "cluster" "centers" "totss" "withinss"
"tot.withinss" "betweenss"
[7] "size"
K-means clustering with 3 clusters of sizes 13, 25, 12
Cluster means:
[,1] [,2]
1 0.9348942 0.22064634
2 2.7112299 -3.95090815
3 -0.6775392 0.01699628
Clustering vector:
[1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 1 1 3 3 1 1 1 1 3 1 1 3 3 1 1 3 1 3 1 3 3 1
[49] 3 3
Within cluster sum of squares by cluster:
[1] 9.878084 32.323548 10.712040
(between_SS / total_SS = 85.2 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss" "betweenss"
[7] "size"
|
|
|
set.seed(7)
x=matrix (rnorm (50*2) , ncol =2)
x[1:25 ,1]=x[1:25 ,1]+3
x[1:25 ,2]=x[1:25 ,2] -4
km.out =kmeans (x,3, nstart =1)
km.out$tot.withinss
km.out =kmeans (x,3, nstart =20)
km.out$tot.withinss
km.out =kmeans (x,3, nstart =50)
km.out$tot.withinss
[1] 72.62232 [1] 57.63156 [1] 57.63156 [1] 72.62232 [1] 57.63156 [1] 57.63156 |
|
|