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Assign clusters to new data

Usage

predict.cluster(clobj, x)

Arguments

clobj Object returned by a clustering algorithm such as kmeans
x Data matrix

Description

Assigns each data point (row in x) the cluster corresponding to the closest center found in clobj.

Value

predict.cluster returns an object of class "cluster". Only size is changed as compared to the argument clobj.
centers The cluster centers.
cluster Vector containing the indices of the clusters where the data is mapped.
initcenters The inital cluster centers.
ncenters The number of cluster centers.
iter The number of iterations performed.
changes The number of changes performed in each iteration step.
size The number of data points in each cluster.

Author(s)

Friedrich Leisch and Andreas Weingessel

See Also

kmeans, predict.cluster

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,verbose=TRUE)
plot(cl,x)   

# a 3-dimensional example
x<-rbind(matrix(rnorm(150,sd=0.3),ncol=3),
         matrix(rnorm(150,mean=1,sd=0.3),ncol=3),
         matrix(rnorm(150,mean=2,sd=0.3),ncol=3))
cl<-kmeans(x,6,20,verbose=TRUE)
plot(cl,x)

# assign classes to some new data
y<-rbind(matrix(rnorm(33,sd=0.3),ncol=3),
         matrix(rnorm(33,mean=1,sd=0.3),ncol=3),
         matrix(rnorm(3,mean=2,sd=0.3),ncol=3))
ycl<-predict(cl, y)
plot(cl,y)