| clusplot.partition {cluster} | R Documentation |
Clusplot (Clustering Plot) method for an object of class partition.
## S3 method for class 'partition': clusplot(x, main = NULL, dist = NULL, ...)
x |
an object of class "partition", e.g. created by 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. |
... |
all optional arguments available for the
clusplot.default function (except for the diss
one) may also be supplied to this function. Graphical parameters
(see par) may also be supplied as arguments to this
function. |
This 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.
An invisible list with components
Distances |
When option lines is 1 or 2 we optain a k by k matrix (k is the number of clusters). The element at row j and column s is the distance between ellipse j and ellipse s. If lines=0, then the value of this component is NA. |
Shading |
A vector of length k (where k is the number of clusters), containing the amount of shading per cluster. Let y be a vector where element i is the ratio between the number of objects in cluster i and the area of ellipse i. When the cluster i is a line segment, y[i] and the density of the cluster are set to NA. Let z be the sum of all the elements of y without the NAs. Then we put shading = y/z *37 + 3. |
clusplot.default for references;
partition.object, pam,
pam.object, clara,
clara.object, fanny,
fanny.object, par.
## 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))