data(iris)
head(iris)
<tr><td>5.1   </td><td>3.5   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>
<tr><td>4.9   </td><td>3.0   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>
<tr><td>4.7   </td><td>3.2   </td><td>1.3   </td><td>0.2   </td><td>setosa</td></tr>
<tr><td>4.6   </td><td>3.1   </td><td>1.5   </td><td>0.2   </td><td>setosa</td></tr>
<tr><td>5.0   </td><td>3.6   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>
<tr><td>5.4   </td><td>3.9   </td><td>1.7   </td><td>0.4   </td><td>setosa</td></tr>
Sepal.Length Sepal.Width Petal.Length Petal.Width Species 
set.seed(42)
km <-kmeans(iris[,1:4], 3,nstart=25)
km
K-means clustering with 3 clusters of sizes 38, 62, 50

Cluster means:
  Sepal.Length Sepal.Width Petal.Length Petal.Width
1     6.850000    3.073684     5.742105    2.071053
2     5.901613    2.748387     4.393548    1.433871
3     5.006000    3.428000     1.462000    0.246000

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 3 3 3 3 3 3 3 3 3 3 3 3
 [38] 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 1 1 1
[112] 1 1 2 2 1 1 1 1 2 1 2 1 2 1 1 2 2 1 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 2 1
[149] 1 2

Within cluster sum of squares by cluster:
[1] 23.87947 39.82097 15.15100
 (between_SS / total_SS =  88.4 %)

Available components:

[1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
[6] "betweenss"    "size"         "iter"         "ifault"      
table(km$cluster, iris$Species)
    setosa versicolor virginica
  1      0          2        36
  2      0         48        14
  3     50          0         0
plot(iris[,1], iris[,2], col=km$cluster)
points(km$centers[,c(1,2)], col=1:3, pch=8, cex=2)