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 |
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 |
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.
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))