hclust

0th

Percentile

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

An object of class 'hclust'. It encodes a stepwise dendrogram.

References

http://danifold.net/fastcluster.html

See Also

fastcluster, hclust.vector, stats::hclust

Aliases
  • hclust
Examples
# 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)
# }
Documentation reproduced from package fastcluster, version 1.1.24, License: FreeBSD | GPL-2 | file LICENSE

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