conversion {flexclust} | R Documentation |
These functions can be used to convert the results from cluster
functions like
kmeans
or pam
to objects
of class "kcca"
and vice versa
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, ...) ## S4 method for signature 'kccasimple,kmeans' coerce(from, to="kmeans", strict=TRUE)
object |
fitted object. |
data |
data which were used to obtain the clustering. For
|
save.data |
Save a copy of the data in the return object? |
k |
number of clusters. |
family |
object of class |
... |
currently not used. |
from, to, strict |
usual arguments for |
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.
Friedrich Leisch
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))