kmeans {mva}  R Documentation 
Perform kmeans clustering on a data matrix.
kmeans(x, centers, iter.max = 10)
x 
A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns). 
centers 
Either the number of clusters or a set of initial cluster centers.
If the first, a random set of rows in x are chosen as the initial
centers.

iter.max 
The maximum number of iterations allowed. 
The data given by x
is clustered by the kmeans algorithm.
When this terminates, all cluster centres are at the mean of
their Voronoi sets (the set of data points which are nearest to
the cluster centre).
The algorithm of Hartigan and Wong (1979) is used.
A list with components:
cluster 
A vector of integers indicating the cluster to which each point is allocated. 
centers 
A matrix of cluster centres. 
withinss 
The withincluster sum of squares for each cluster. 
size 
The number of points in each cluster. 
Hartigan, J.A. and Wong, M.A. (1979). A Kmeans clustering algorithm. Applied Statistics 28, 100–108.
# a 2dimensional example x < rbind(matrix(rnorm(100, sd = 0.3), ncol = 2), matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)) cl < kmeans(x, 2, 20) plot(x, col = cl$cluster) points(cl$centers, col = 1:2, pch = 8)