# a 2-dimensional example
x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
           matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
#
kmeans_dist = data.frame(num=1:50,error=1:50,x1=1:50,y1=1:50,x2=1:50,y2=1:50)
#
options(warn=-1)
for (i in 1:50){
  kmeans_dist$num[i]=i
  cl <- kmeans(x, 2,nstart=20)
  kmeans_dist$error[i]=cl$withinss
  kmeans_dist$x1[i]=cl$centers[1]
  kmeans_dist$y1[i]=cl$centers[2]
  kmeans_dist$x2[i]=cl$centers[3]
  kmeans_dist$y2[i]=cl$centers[4]
}
optimize_center = kmeans_dist[which.min(kmeans_dist$error),]
center= matrix(c(optimize_center[,"x1"],optimize_center[,"x2"],optimize_center[,"y1"],optimize_center[,"y2"]),nrow=2,
               ncol=2,byrow=TRUE,dimnames=list(c("row1","row2"),c("x","y")))
cl_best <- kmeans(x, centers=center,2)
cl_best

## K-means clustering with 2 clusters of sizes 49, 51
## 
## Cluster means:
##              x           y
## 1 -0.007721516 -0.02078729
## 2  0.991086783  1.03941709
## 
## Clustering vector:
##   [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 1 1 1 1 1 1
##  [36] 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 2 2 2 2 2 2 2 2 2 2
##  [71] 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 2 2 2 2 2
## 
## Within cluster sum of squares by cluster:
## [1]  8.471624 10.733281
##  (between_SS / total_SS =  73.4 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"    
## [5] "tot.withinss" "betweenss"    "size"         "iter"        
## [9] "ifault"