\HeaderA{summary.clara}{Summary Method for `clara' Objects}{summary.clara}
\aliasA{print.summary.clara}{summary.clara}{print.summary.clara}
\keyword{cluster}{summary.clara}
\keyword{print}{summary.clara}
\begin{Description}\relax
Returns (and prints) a summary list for a \code{clara} object.
Printing gives more output than the corresponding
\code{\LinkA{print.clara}{print.clara}} method.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'clara':
summary(object, ...)
## S3 method for class 'summary.clara':
print(x, ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x, object}] a \code{\LinkA{clara}{clara}} object.
\item[\code{...}] potential further arguments (require by generic).
\end{ldescription}
\end{Arguments}
\begin{SeeAlso}\relax
\code{\LinkA{clara.object}{clara.object}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## generate 2000 objects, divided into 5 clusters.
set.seed(47)
x <- rbind(cbind(rnorm(400, 0,4), rnorm(400, 0,4)),
           cbind(rnorm(400,10,8), rnorm(400,40,6)),
           cbind(rnorm(400,30,4), rnorm(400, 0,4)),
           cbind(rnorm(400,40,4), rnorm(400,20,2)),
           cbind(rnorm(400,50,4), rnorm(400,50,4))
)
clx5 <- clara(x, 5)
## Mis`classification' table:
table(rep(1:5, rep(400,5)), clx5$clust) # -> 1 "error"
summary(clx5)

## Graphically:
par(mfrow = c(3,1), mgp = c(1.5, 0.6, 0), mar = par("mar") - c(0,0,2,0))
plot(x, col = rep(2:6, rep(400,5)))
plot(clx5)
\end{ExampleCode}
\end{Examples}

