| clusplot.partition {cluster} | R Documentation |
Creates the clusplot of a partition object.
clusplot.partition(x, ...)
x |
an object of class "partition", e.g. created by the functions pam,
clara, or fanny.
All optional arguments available to the function clusplot.default (except for the diss option) may also be supplied to this function.
Graphical parameters (see par) may also
be supplied as arguments to this function.
|
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 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.
An invisible list with components:
a visual display of the clustering is plotted on the current graphics device.
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.
partition.object, pam, pam.object, clara, clara.object, fanny,
fanny.object, par, 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))