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\name{partition.object}
\alias{partition}% == class
\alias{partition.object}
\title{Partitioning Object}
\description{
The objects of class \code{"partition"} represent a partitioning of a
dataset into clusters.
}
\section{GENERATION}{
These objects are returned from \code{pam}, \code{clara} or \code{fanny}.
}
\section{METHODS}{
The \code{"partition"} class has a method for the following generic functions:
\code{plot}, \code{clusplot}.
}
\section{INHERITANCE}{
The following classes inherit from class \code{"partition"} :
\code{"pam"}, \code{"clara"} and \code{"fanny"}.
See \code{\link{pam.object}}, \code{\link{clara.object}} and
\code{\link{fanny.object}} for details.
}
\value{a \code{"partition"} object is a list with the following
(and typically more) components:
\item{clustering}{
the clustering vector. An integer vector of length \eqn{n}, the number of
observations, giving for each observation the number ('id') of the
cluster to which it belongs.}
\item{call}{the matched \code{\link{call}} generating the object.}
\item{silinfo}{
a list with all \emph{silhouette} information, only available when
the number of clusters is non-trivial, i.e., \eqn{1 < k < n} and
then has the following components, see \code{\link{silhouette}}
\describe{
\item{widths}{an (n x 3) matrix, as returned by
\code{\link{silhouette}()}, with for each observation i the
cluster to which i belongs, as well as the neighbor cluster of i
(the cluster, not containing i, for which the average
dissimilarity between its observations and i is minimal), and
the silhouette width \eqn{s(i)} of the observation.
}
\item{clus.avg.widths}{the average silhouette width per cluster.}
\item{avg.width}{the average silhouette width for the dataset, i.e.,
simply the average of \eqn{s(i)} over all observations \eqn{i}.}
}% describe
This information is also needed to construct a \emph{silhouette plot} of
the clustering, see \code{\link{plot.partition}}.
Note that \code{avg.width} can be maximized over different
clusterings (e.g. with varying number of clusters) to choose an
\emph{optimal} clustering.%% see an example or a demo << FIXME >>
}
\item{objective}{value of criterion maximized during the
partitioning algorithm, may more than one entry for different stages.}
\item{diss}{
an object of class \code{"dissimilarity"}, representing the total
dissimilarity matrix of the dataset (or relevant subset, e.g. for
\code{clara}).
}
\item{data}{
a matrix containing the original or standardized data. This might
be missing to save memory or when a dissimilarity matrix was given
as input structure to the clustering method.
}
}
\seealso{\code{\link{pam}}, \code{\link{clara}}, \code{\link{fanny}}.
}
\keyword{cluster}
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