\HeaderA{clustOutput-class}{Class "clustOutput" container for cluster analysis results}{clustOutput.Rdash.class}
\aliasA{bclust-class}{clustOutput-class}{bclust.Rdash.class}
\aliasA{fclust-class}{clustOutput-class}{fclust.Rdash.class}
\aliasA{hclust-class}{clustOutput-class}{hclust.Rdash.class}
\aliasA{show,clustOutput-method}{clustOutput-class}{show,clustOutput.Rdash.method}
\aliasA{silhouetteB,clustOutput-method}{clustOutput-class}{silhouetteB,clustOutput.Rdash.method}
\keyword{classes}{clustOutput-class}
\begin{Description}\relax
container for cluster analysis results
\end{Description}
\begin{Section}{Objects from the Class}
Objects can be created by calls of the form \code{new("clustOutput", ...)}.
\end{Section}
\begin{Section}{Slots}
\describe{
\item[\code{clustIndices}:] Object of class \code{\LinkA{MLLabel}{MLLabel}}  
\item[\code{clustScores}:] Object of class \code{\LinkA{MLScore}{MLScore}}, when
a numerical figure of clustering is available  
\item[\code{method}:] Object of class \code{"character"} tag indicating
name of procedure 
\item[\code{RObject}:] Object of class \code{"ANY"} unprocessed output of the
clustering function 
\item[\code{call}:] Object of class \code{"call"} audit of the call that
created the object 
\item[\code{distMat}:] Object of class \code{"dist"} distance among objects 
}
\end{Section}
\begin{Section}{Extends}
Class \code{"\LinkA{MLOutput}{MLOutput.Rdash.class}"}, directly.
\end{Section}
\begin{Section}{Methods}
\describe{
\item[show] \code{signature(object = "clustOutput")}: concise report 
\item[silhouetteB] \code{signature(out = "clustOutput")}: compute the \code{\LinkA{silhouette}{silhouette}}
object corresponding to the partition 
}
\end{Section}
\begin{Author}\relax
VJ Carey <stvjc@channing.harvard.edu>
\end{Author}
\begin{SeeAlso}\relax
\code{"\LinkA{classifOutput}{classifOutput.Rdash.class}"}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
showClass("clustOutput")

 library(golubEsets)
 data(Golub_Merge)
 train <- c(sample(1:47, 23), sample(48:72, 12))
 agOut <- agnesB(Golub_Merge[100:200,], 3)
 agOut
 sil.ag <- silhouetteB(agOut)
 plot(sil.ag, main="Silhouette Plot for Agnes Clustering")

\end{ExampleCode}
\end{Examples}

