Example of K-Means clustering for k=2
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)
plot(x, col =(km.out$cluster +1) , main="K-Means Clustering
Results with K=2", xlab ="", ylab="", pch =20, cex =2)
[1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [49] 1 1 [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [49] 1 1  
|
Example of K-Means clustering for k=3
set.seed (3)
x=matrix (rnorm (50*2) , ncol =2)
x[1:25 ,1]=x[1:25 ,1]+3
x[1:25 ,2]=x[1:25 ,2] -4
km.out =kmeans (x,3, nstart =20)
km.out$cluster
plot(x, col =(km.out$cluster +1) , main="K-Means Clustering
Results with K=2", xlab ="", ylab="", pch =20, cex =2)
plot(x, col =(km.out$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 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 [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  
|
|
|