conversion {flexclust}R Documentation

Conversion Between S3 Partition Objects and KCCA

Description

These functions can be used to convert the results from cluster functions like kmeans or pam to objects of class "kcca" and vice versa

Usage

as.kcca(object, ...)

## S3 method for class 'hclust'
as.kcca(object, data, k, family=NULL, save.data=FALSE, ...)
## S3 method for class 'kmeans'
as.kcca(object, data, save.data=FALSE, ...)
## S3 method for class 'partition'
as.kcca(object, data=NULL, save.data=FALSE, ...)
## S3 method for class 'skmeans'
as.kcca(object, data, save.data=FALSE, ...)
## S4 method for signature 'kccasimple,kmeans'
coerce(from, to="kmeans", strict=TRUE)

Arguments

object

fitted object.

data

data which were used to obtain the clustering. For "partition" objects created by functions from package cluster this is optional, if object contains the data.

save.data

Save a copy of the data in the return object?

k

number of clusters.

family

object of class kccaFamily, can be omitted for some known distances.

...

currently not used.

from, to, strict

usual arguments for coerce

Details

For hierarchical clustering the cluster memberships of the converted object can be different from the result of cutree, because one KCCA-iteration has to be performed in order to obtain a valid kcca object. In this case a warning is issued.

Author(s)

Friedrich Leisch

Examples

data(Nclus)

cl1 <- kmeans(Nclus, 4)
cl1
cl1a <- as.kcca(cl1, Nclus)
cl1a
cl1b <- as(cl1a, "kmeans")



library("cluster")
cl2 <- pam(Nclus, 4)
cl2
cl2a <- as.kcca(cl2)
cl2a
## the same
cl2b = as.kcca(cl2, Nclus)
cl2b



## hierarchical clustering
hc <- hclust(dist(USArrests))
plot(hc)
rect.hclust(hc, k=3)
c3 <- cutree(hc, k=3)
k3 <- as.kcca(hc, USArrests, k=3)
barchart(k3)
table(c3, clusters(k3))
[Package flexclust version 1.3-4 Index]