Computes the “agglomerative coefficient” (aka “divisive
diana), measuring the
clustering structure of the dataset.
For each observation i, denote by m(i) its dissimilarity to the first cluster it is merged with, divided by the dissimilarity of the merger in the final step of the algorithm. The agglomerative coefficient is the average of all 1 - m(i). It can also be seen as the average width (or the percentage filled) of the banner plot.
coefHier() directly interfaces to the underlying C code, and
“proves” that only
object$heights is needed to
compute the coefficient.
Because it grows with the number of observations, this measure should not be used to compare datasets of very different sizes.
coefHier(object) coef.hclust(object, ...) ## S3 method for class 'hclust' coef(object, ...) ## S3 method for class 'twins' coef(object, ...)
an object of class
currently unused potential further arguments
a number specifying the agglomerative (or divisive for
diana objects) coefficient as defined by Kaufman and Rousseeuw,
agnes.object $ ac or
diana.object $ dc.
data(agriculture) aa <- agnes(agriculture) coef(aa) # really just extracts aa$ac coef(as.hclust(aa))# recomputes coefHier(aa) # ditto
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