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Tip revision: c27795bfae1e3309def7b5fa022d60a7ca28b75a authored by Catherine Hurley on 07 January 2019, 19:00 UTC
version 1.3.2
Tip revision: c27795b

\title{ Cluster heterogeneity of 2-d data }
Computes measures of cluster heterogeneity of 2-d data,
where \code{x} and  \code{y} give the object coordinates.
diameter(x, y, ...)
star(x, y, ...)
gtot(x,y, ...)
gave(x,y, ...)
  \item{x}{is a numeric vector. }
  \item{y}{is a numeric vector. }
  \item{\dots}{are passed to \code{dist}. }
\code{diameter} computes the cluster diameter- the maximum distance
between objects. 

\code{star} computes the cluster star distance- the smallest
total distance from one object to another.

\code{km2} computes the kmeans distance.

\code{gtot} computes the sum of all inter-object distances.

\code{gave} computes the per-object average of all 
inter-object distances.

\value{The cluster measure is returned.
\references{ See Gordon, A. D. (1999).``Classification''. Second Edition. London:
     Chapman and Hall / CRC }
\author{ Catherine B. Hurley}

\seealso{ \code{\link{colpairs}}, \code{\link{cpairs}}, \code{\link{order.single}}}
x <- runif(20)
y <- runif(20)

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