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\name{clusplot.partition}
\alias{clusplot.partition}
\title{Bivariate Clusplot of a Partitioning Object}
\description{
Clusplot (Clustering Plot) method for an object of class \code{partition}.
}
\usage{
\method{clusplot}{partition}(x, main = NULL, dist = NULL, \dots)
}
\arguments{
\item{x}{
an object of class \code{"partition"}, e.g. created by the functions
\code{\link{pam}}, \code{\link{clara}}, or \code{\link{fanny}}.}
\item{main}{title for the plot; when \code{NULL} (by default), a title
is constructed, using \code{x$call}.}
\item{dist}{when \code{x} does not have a \code{diss} nor a
\code{data} component, e.g., for \code{\link{pam}(dist(*),
keep.diss=FALSE)}, \code{dist} must specify the dissimilarity for the
clusplot.}
\item{\dots}{all optional arguments available for the
\code{\link{clusplot.default}} function (except for the \code{diss}
one) may also be supplied to this function. Graphical parameters
(see \code{\link{par}}) may also be supplied as arguments to this
function.}
}
\value{
An invisible list with components
\item{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.
}
\item{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.
}
}
\details{
This \code{clusplot.partition()} method relies on
\code{\link{clusplot.default}}.
If the clustering algorithms \code{pam}, \code{fanny} and \code{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 \code{\link{pam}} or \code{\link{fanny}}, the dissimilarity
matrix will be given as input to \code{clusplot}. When the clustering
algorithm \code{\link{clara}} was applied to a data matrix with NAs
then clusplot will replace the missing values as described in
\code{\link{clusplot.default}}, because a dissimilarity matrix is not
available.
}
\seealso{\code{\link{clusplot.default}} for references;
\code{\link{partition.object}}, \code{\link{pam}},
\code{\link{pam.object}}, \code{\link{clara}},
\code{\link{clara.object}}, \code{\link{fanny}},
\code{\link{fanny.object}}, \code{\link{par}}.
}
\examples{
## 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))
}
\keyword{cluster}
\keyword{hplot}
|
