fastcluster (version 1.1.24)

hclust: Fast hierarchical, agglomerative clustering of dissimilarity data

Description

This function implements hierarchical clustering with the same interface as hclust from the stats package but with much faster algorithms.

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.

Value

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

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').

References

http://danifold.net/fastcluster.html

See Also

fastcluster, hclust.vector, stats::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)
# }

Run the code above in your browser using DataCamp Workspace