clusplot.partition: Bivariate Cluster Plot (of a Partitioning Object)

In cluster: "Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.

Bivariate Cluster Plot (of a Partitioning Object)

Description

Draws a 2-dimensional “clusplot” (clustering plot) on the current graphics device. The generic function has a default and a partition method.

Usage

clusplot(x, ...)

## S3 method for class 'partition'
clusplot(x, main = NULL, dist = NULL, ...)

Arguments

x	an R object, here, specifically an object of class "partition", e.g. created by one of the functions pam, clara, or fanny.
main	title for the plot; when NULL (by default), a title is constructed, using x$call.
dist	when x does not have a diss nor a data component, e.g., for pam(dist(*), keep.diss=FALSE), dist must specify the dissimilarity for the clusplot.
...	optional arguments passed to methods, notably the clusplot.default method (except for the diss one) may also be supplied to this function. Many graphical parameters (see par) may also be supplied as arguments here.

Details

The clusplot.partition() method relies on clusplot.default.

If the clustering algorithms pam, fanny and clara are applied to a data matrix of observations-by-variables then a clusplot of the resulting clustering can always be drawn. When the data matrix contains missing values and the clustering is performed with pam or fanny, the dissimilarity matrix will be given as input to clusplot. When the clustering algorithm clara was applied to a data matrix with NAs then clusplot() will replace the missing values as described in clusplot.default, because a dissimilarity matrix is not available.

Value

For the partition (and default) method: An invisible list with components Distances and Shading, as for clusplot.default, see there.

Side Effects

a 2-dimensional clusplot is created on the current graphics device.

See Also

clusplot.default for references; partition.object, pam, pam.object, clara, clara.object, fanny, fanny.object, par.

Examples

 ## For more, see ?clusplot.default

## generate 25 objects, divided into 2 clusters.
x <- rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)),
           cbind(rnorm(15,5,0.5), rnorm(15,5,0.5)))
clusplot(pam(x, 2))
## add noise, and try again :
x4 <- cbind(x, rnorm(25), rnorm(25))
clusplot(pam(x4, 2))

cluster documentation built on Nov. 28, 2023, 1:07 a.m.