kmeansruns {fpc} | R Documentation |
This calls the function kmeans
to perform a k-means
clustering, but initializes the k-means algorithm several times with
random points from the data set as means. Furthermore, it is more
robust against the occurrence of empty clusters in the algorithm.
kmeansruns(data,k,iter.max=100,runs=100,scaledata=FALSE,plot=FALSE)
data |
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). |
k |
integer. The number of clusters. |
iter.max |
integer. The maximum number of iterations allowed. |
runs |
integer. Number of starts of the k-means algorithm. |
scaledata |
logical. If TRUE , the variables are centered
and scaled to unit variance before execution. |
plot |
logical. If TRUE , every clustering resulting from a
run of the algorithm is plotted. |
The output of the optimal run of the kmeans
-function.
A list with components
cluster |
A vector of integers indicating the cluster to which each point is allocated. |
centers |
A matrix of cluster centers. |
withinss |
The within-cluster sum of squares for each cluster. |
size |
The number of points in each cluster. |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
set.seed(20000) face <- rFace(50,dMoNo=2,dNoEy=0,p=2) kmr1 <- kmeansruns(face,k=5,runs=1) kmr5 <- kmeansruns(face,k=5,runs=5)