# hclust

From fastcluster v1.1.22
by Daniel Mllner

##### Fast hierarchical, agglomerative clustering of dissimilarity data

This function implements hierarchical clustering with the same interface as `hclust`

from the stats package but with much faster algorithms.

- Keywords
- multivariate, cluster

##### Usage

`hclust(d, method="complete", members=NULL)`

##### Arguments

- d
- a dissimilarity structure as produced by
`dist`

. - method
- the agglomeration method to be used. This must be (an
unambiguous abbreviation of) one of
`"single"`

,`"complete"`

,`"average"`

,`"mcquitty"`

,`"ward.D"`

,`"ward.D2"`

,`"centroid"`

or`"median"`

. - members
`NULL`

or a vector with length the number of observations.

##### Details

See the documentation of the original function
`hclust`

in the stats package.

A comprehensive User's manual
fastcluster.pdf is available as a vignette. Get this from the R command line with `vignette('fastcluster')`

.

##### Value

`'hclust'`

. It encodes a stepwise dendrogram.##### References

##### See Also

##### Examples

```
#
# hclust(...) # new method
# stats::hclust(...) # old method
require(fastcluster)
require(graphics)
hc <- hclust(dist(USArrests), "ave")
plot(hc)
plot(hc, hang = -1)
## Do the same with centroid clustering and squared Euclidean distance,
## cut the tree into ten clusters and reconstruct the upper part of the
## tree from the cluster centers.
hc <- hclust(dist(USArrests)^2, "cen")
memb <- cutree(hc, k = 10)
cent <- NULL
for(k in 1:10){
cent <- rbind(cent, colMeans(USArrests[memb == k, , drop = FALSE]))
}
hc1 <- hclust(dist(cent)^2, method = "cen", members = table(memb))
opar <- par(mfrow = c(1, 2))
plot(hc, labels = FALSE, hang = -1, main = "Original Tree")
plot(hc1, labels = FALSE, hang = -1, main = "Re-start from 10 clusters")
par(opar)
```

*Documentation reproduced from package fastcluster, version 1.1.22, License: FreeBSD | GPL-2 | file LICENSE*

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