plot.partition {cluster}R Documentation

Plot of a Partition of the Data Set


Creates plots for visualizing a partition object.


## S3 method for class 'partition':
plot(x, ask = FALSE, which.plots = NULL,
               nmax.lab = 40, max.strlen = 5, data = x$data, dist = NULL,
               cor = TRUE, stand = FALSE, lines = 2,
               shade = FALSE, color = FALSE, labels = 0, plotchar = TRUE,
               span = TRUE, xlim = NULL, ylim = NULL, main = NULL, ...)


x an object of class "partition", typically created by the functions pam, clara, or fanny.
ask logical; if true and which.plots is NULL, plot.partition operates in interactive mode, via menu.
which.plots integer vector or NULL (default), the latter producing both plots. Otherwise, which.plots must contain integers of 1 for a clusplot or 2 for silhouette.
nmax.lab integer indicating the number of labels which is considered too large for single-name labeling the silhouette plot.
max.strlen positive integer giving the length to which strings are truncated in silhouette plot labeling.
data numeric matrix with the scaled data; per default taken from the partition object x, but can be specified explicitly.
dist when x does not have a diss component as for pam(*, keep.diss=FALSE), dist must be the dissimilarity if a clusplot is desired.
cor,stand,lines,shade,color,labels,plotchar,span,xlim,ylim,main, ... All optional arguments available for the clusplot.default function (except for the diss one) and graphical parameters (see par) may also be supplied as arguments to this function.


When ask= TRUE, rather than producing each plot sequentially, plot.partition displays a menu listing all the plots that can be produced. If the menu is not desired but a pause between plots is still wanted, call par(ask= TRUE) before invoking the plot command.

The clusplot of a cluster partition consists of a two-dimensional representation of the observations, in which the clusters are indicated by ellipses (see clusplot.partition for more details).

The silhouette plot of a nonhierarchical clustering is fully described in Rousseeuw (1987) and in chapter 2 of Kaufman and Rousseeuw (1990). For each observation i, a bar is drawn, representing its silhouette width s(i), see silhouette for details. Observations are grouped per cluster, starting with cluster 1 at the top. Observations with a large s(i) (almost 1) are very well clustered, a small s(i) (around 0) means that the observation lies between two clusters, and observations with a negative s(i) are probably placed in the wrong cluster.

A clustering can be performed for several values of k (the number of clusters). Finally, choose the value of k with the largest overall average silhouette width.

Side Effects

An appropriate plot is produced on the current graphics device. This can be one or both of the following choices:
Silhouette plot


In the silhouette plot, observation labels are only printed when the number of observations is less than nmax.lab (40, by default), for readability. Moreover, observation labels are truncated to maximally max.strlen (5) characters.
For more flexibility, use plot(silhouette(x), ...), see plot.silhouette.


Rousseeuw, P.J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53–65.

Further, the references in plot.agnes.

See Also

partition.object, clusplot.partition, clusplot.default, 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)))
plot(pam(x, 2))

## Save space not keeping data in clus.object, and still clusplot() it:
cx <- clara(xclara, 3, = FALSE)
cx$data # is NULL
plot(cx, data = xclara)

[Package cluster version 1.9.8 Index]