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\name{clusplot}\alias{clusplot}\alias{clusplot.partition}\title{Bivariate Cluster Plot (of a Partitioning Object)}\description{Draws a 2-dimensional \dQuote{clusplot} (clustering plot) on thecurrent graphics device.The generic function has a default and a \code{partition} method.}\usage{clusplot(x, \dots)\method{clusplot}{partition}(x, main = NULL, dist = NULL, \dots)}\arguments{\item{x}{an \R object, here, specifically an object of class\code{"partition"}, e.g. created by one of the functions\code{\link{pam}}, \code{\link{clara}}, or \code{\link{fanny}}.}\item{main}{title for the plot; when \code{NULL} (by default), a titleis 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 theclusplot.}\item{\dots}{optional arguments passed to methods, notably the\code{\link{clusplot.default}} method (except for the \code{diss}one) may also be supplied to this function. Many graphical parameters(see \code{\link{par}}) may also be supplied as arguments here.}}\section{Side Effects}{a 2-dimensional clusplot is created on the current graphics device.}\value{For the \code{partition} (and \code{default}) method: An invisiblelist with components \code{Distances} and \code{Shading}, as for\code{\link{clusplot.default}}, see there.}\details{The \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 aclusplot of the resulting clustering can always be drawn. When thedata matrix contains missing values and the clustering is performedwith \code{\link{pam}} or \code{\link{fanny}}, the dissimilaritymatrix will be given as input to \code{clusplot}. When the clusteringalgorithm \code{\link{clara}} was applied to a data matrix with \code{\link{NA}}sthen \code{clusplot()} will replace the missing values as described in\code{\link{clusplot.default}}, because a dissimilarity matrix is notavailable.}\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{ ## For more, see ?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))## add noise, and try again :x4 <- cbind(x, rnorm(25), rnorm(25))clusplot(pam(x4, 2))}\keyword{cluster}\keyword{hplot}