partition
object.
clusplot.partition(x, ...)
"partition"
,
e.g. created by the functions
pam
,
clara
, or
fanny
.
diss
option)
may also be supplied to this function.
Graphical parameters may also be supplied as arguments to
this function (see
).
clusplot uses the functions princomp and cmdscale. These
functions are data reduction techniques. They will
represent the data in a bivariate plot. Ellipses are
then drawn to indicate the clusters. The further layout
of the plot is determined by the optional arguments.
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.
Kaufman, L. and Rousseeuw, P. J. (1990).
Finding Groups in Data: An Introduction to Cluster Analysis.
Wiley, New York.
Pison, G., Struyf, A. and Rousseeuw, P. J. (1997).
Displaying a Clustering with CLUSPLOT.
Technical Report, University of Antwerp, submitted.
Struyf, A., Hubert, M. and Rousseeuw, P. J. (1997).
Integrating robust clustering techniques in S-PLUS.
Computational Statistics and Data Analysis,
26, 17-37.
# 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))