# 3-mean(Lkhagva)

## 2224 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"