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))