K-Means Clustering

Usage

kmeans(x, centers, iter.max=10)

Arguments

x A data frame or matrix of data.
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.

Description

The data given by x is clustered by the k-Means algorithm. This algorithm works by repeatedly moving all cluster centers to the mean of their Voronoi sets.

The algorithm stops, if no cluster center has changed during the last iteration or the maximum number of iterations (given by iter.max) is reached.

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 within-cluster sum of squares for each cluster.
size The number of points in each cluster

References

Hartigan, J.A. and Wong, M.A. (1979). A K-means clustering algorithm. Applied Statistics 28, 100-108.

Examples

# a 2-dimensional 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)

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