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

from the stats package but with much faster algorithms.

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

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.

An object of class `'hclust'`

. It encodes a stepwise dendrogram.

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')`

.

# NOT RUN { # Taken and modified from stats::hclust # # 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) # }