\name{diameter} \alias{diameter} \alias{star} \alias{km2} \alias{gtot} \alias{gave} \title{ Cluster heterogeneity of 2-d data } \description{ Computes measures of cluster heterogeneity of 2-d data, where \code{x} and \code{y} give the object coordinates. } \usage{ diameter(x, y, ...) star(x, y, ...) km2(x,y) gtot(x,y, ...) gave(x,y, ...) } \arguments{ \item{x}{is a numeric vector. } \item{y}{is a numeric vector. } \item{\dots}{are passed to \code{dist}. } } \details{ \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{pairs.bg}}, \code{\link{order.single}}} \examples{ x <- runif(20) y <- runif(20) diameter(x,y) } \keyword{cluster}