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\name{ExClust-class}
\docType{class}
\alias{ExClust-class}
\alias{ExClust}
\alias{exclust}
\alias{[,ExClust,index,missing,missing-method}
\alias{[[,ExClust,index,missing-method}
\alias{length,ExClust-method}
\alias{similarity,ExClust-method}
\title{Class "ExClust"}
\description{S4 class for storing exemplar-based clusterings}
\section{Objects}{
Objects of this class can be created by calling \code{\link{cutree}}
to cut out a clustering level from a cluster hierarchy
of class \code{\linkS4class{AggExResult}}. Moreover,
\code{\link{cutree}} can also be used to convert an object of
class \code{\linkS4class{APResult}} to class \code{ExClust}.
}
\section{Slots}{
The following slots are defined for \link{ExClust} objects:
\describe{
\item{\code{l}:}{number of samples in the data set}
\item{\code{sel}:}{subset of samples used for leveraged clustering}
\item{\code{exemplars}:}{vector containing indices of exemplars}
\item{\code{clusters}:}{list containing the clusters; the i-th
component is a vector of indices of
data points belonging to the i-th
exemplar (including the exemplar itself)}
\item{\code{idx}:}{vector of length \code{l} realizing a
sample-to-exemplar mapping; the i-th entry
contains the index of the exemplar the i-th
sample belongs to}
\item{\code{sim}:}{similarity matrix; only available if
the preceding clustering method was called with
\code{includeSim=TRUE}.}
\item{\code{call}:}{method call of the preceding clustering method}
}
}
\section{Methods}{
\describe{
\item{plot}{\code{signature(x="ExClust")}: see
\code{\link{plot-methods}}}
\item{plot}{\code{signature(x="ExClust", y="matrix")}: see
\code{\link{plot-methods}}}
\item{heatmap}{\code{signature(x="ExClust")}: see
\code{\link{heatmap-methods}}}
\item{heatmap}{\code{signature(x="ExClust", y="matrix")}: see
\code{\link{heatmap-methods}}}
\item{show}{\code{signature(object="ExClust")}: see
\code{\link{show-methods}}}
\item{labels}{\code{signature(object="ExClust")}: see
\code{\link{labels-methods}}}
\item{cutree}{\code{signature(object="ExClust", k="ANY", h="ANY")}: see
\code{\link{cutree-methods}}}
\item{length}{\code{signature(x="ExClust")}: gives the number of
clusters.}
\item{sort}{\code{signature(x="ExClust")}: see
\code{\link{sort-methods}}}
\item{as.hclust}{\code{signature(x="ExClust")}: see
\code{\link{coerce-methods}}}
\item{as.dendrogram}{\code{signature(object="ExClust")}: see
\code{\link{coerce-methods}}}
}
}
\section{Accessors}{
In the following code snippets, \code{x} is an \code{ExClust} object.
\describe{
\item{[[}{\code{signature(x="ExClust", i="index", j="missing")}:
\code{x[[i]]} returns the i-th cluster as a list of indices
of samples belonging to the i-th cluster.
}
\item{[}{\code{signature(x="ExClust", i="index", j="missing",
drop="missing")}: \code{x[i]} returns a list of integer vectors with the
indices of samples belonging to this cluster. The list has as
many components as the argument \code{i} has elements. A list is
returned even if \code{i} is a single integer.
}
\item{similarity}{\code{signature(x="ExClust")}: gives the similarity
matrix.
}
}
}
\author{Ulrich Bodenhofer, Andreas Kothmeier, and Johannes Palme}
\references{\url{https://github.com/UBod/apcluster}
Bodenhofer, U., Kothmeier, A., and Hochreiter, S. (2011)
APCluster: an R package for affinity propagation clustering.
\emph{Bioinformatics} \bold{27}, 2463-2464.
DOI: \doi{10.1093/bioinformatics/btr406}.
}
\seealso{\code{\link{aggExCluster}}, \code{\link{show-methods}},
\code{\link{plot-methods}}, \code{\link{labels-methods}},
\code{\link{cutree-methods}}, \code{\linkS4class{AggExResult}},
\code{\linkS4class{APResult}}}
\examples{
## create two Gaussian clouds
cl1 <- cbind(rnorm(20, 0.2, 0.05), rnorm(20, 0.8, 0.06))
cl2 <- cbind(rnorm(25, 0.7, 0.08), rnorm(25, 0.3, 0.05))
x <- rbind(cl1, cl2)
## compute similarity matrix (negative squared Euclidean)
sim <- negDistMat(x, r=2)
## run affinity propagation
aggres <- aggExCluster(sim)
## extract level with two clusters
excl <- cutree(aggres, k=2)
## show details of clustering results
show(excl)
## plot information about clustering run
plot(excl, x)
}
\keyword{classes}
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