Kmeans {amap}  R Documentation 
KMeans Clustering
Description
Perform kmeans clustering on a data matrix.
Usage
Kmeans(x, centers, iter.max = 10, nstart = 1,
method = "euclidean")
Arguments
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). Or an object of class "exprSet". 
centers 
Either the number of clusters or a set of initial cluster centers.
If the first, a random set of rows in 
iter.max 
The maximum number of iterations allowed. 
nstart 
If 
method 
the distance measure to be used. This must be one of

Details
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 Lloyd–Forgy is used; method="euclidean" should return same result as with function kmeans.
Value
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 square distances for each cluster. 
size 
The number of points in each cluster. 
Note
An objective: to allow NA values.
See Also
Examples
## a 2dimensional 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")
(cl < Kmeans(x, 2))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:2, pch = 8, cex=2)
## random starts do help here with too many clusters
(cl < Kmeans(x, 5, nstart = 25))
plot(x, col = cl$cluster)
points(cl$centers, col = 1:5, pch = 8)
Kmeans(x, 5,nstart = 25, method="abscorrelation")