# coef.hclust

##### Agglomerative / Divisive Coefficient for 'hclust' Objects

Computes the “agglomerative coefficient” (aka “divisive
coefficient” for `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.

- Keywords
- cluster

##### Usage

```
coefHier(object)
coef.hclust(object, …)
# S3 method for hclust
coef(object, …)
# S3 method for twins
coef(object, …)
```

##### Arguments

- object
an object of class

`"hclust"`

or`"twins"`

, i.e., typically the result of`hclust(.)`

,`agnes(.)`

, or`diana(.)`

.Since

`coef.hclust`

only uses`object$heights`

, and`object$merge`

,`object`

can be any list-like object with appropriate`merge`

and`heights`

components.For

`coefHier`

, even only`object$heights`

is needed.- …
currently unused potential further arguments

##### Value

a number specifying the *agglomerative* (or *divisive* for
`diana`

objects) coefficient as defined by Kaufman and Rousseeuw,
see `agnes.object $ ac`

or `diana.object $ dc`

.

##### Examples

```
# NOT RUN {
data(agriculture)
aa <- agnes(agriculture)
coef(aa) # really just extracts aa$ac
coef(as.hclust(aa))# recomputes
coefHier(aa) # ditto
# }
```

*Documentation reproduced from package cluster, version 2.0.7-1, License: GPL (>= 2)*