3-mean(Lkhagva)

1843 days ago by bigdata2016

set.seed (4) 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 plot(x, col =(km.out$cluster +1) , main="K-Means Clustering Results with K=3", xlab ="", ylab="", pch =20, cex =2) 
       
K-means clustering with 3 clusters of sizes 14, 11, 25

Cluster means:
        [,1]        [,2]
1 -0.7532448 -0.39907658
2  0.8968634  0.05541174
3  3.4968274 -3.88442866

Clustering vector:
 [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 1 2 2 2 1 1
2 1 1 2 1 1 2 1 2 1 1 1 2 1
[49] 2 1

Within cluster sum of squares by cluster:
[1]  9.796515 14.391060 33.865779
 (between_SS / total_SS =  85.5 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"    
"tot.withinss" "betweenss"   
[7] "size"        
K-means clustering with 3 clusters of sizes 14, 11, 25

Cluster means:
        [,1]        [,2]
1 -0.7532448 -0.39907658
2  0.8968634  0.05541174
3  3.4968274 -3.88442866

Clustering vector:
 [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 2 1 2 2 2 1 1 2 1 1 2 1 1 2 1 2 1 1 1 2 1
[49] 2 1

Within cluster sum of squares by cluster:
[1]  9.796515 14.391060 33.865779
 (between_SS / total_SS =  85.5 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss" "betweenss"   
[7] "size"