Package hclust1d
(Hierarchical CLUSTering for 1D) is a
suit of algorithms for univariate agglomerative hierarchical clustering
(with a comprehensive list of choices of a linkage function, please
consult supported_methods
for the current list) in \(\mathcal{O}(n\log n)\) time.
The better algorithmic time complexity (compared to multidimensional
hierarchical clustering) paired with its efficient C++
implementation make hclust1d
very fast. The computational
time beats stats::hclust
on all sizes of data and is en
par with fastcluster::hclust
with small data sizes.
However, it is of orders of magnitude faster than both multivariate
clustering routines on larger data sizes.
The output of hclust1d
is of the same S3 class and
format as the outputs of stats::hclust
or
fastcluster::hclust
and thus the resulting clustering can
be further investigated with standard calls to print
,
plot
(plots a dendrogram), etc. In fact, for 1D cases the
call to hclust
can be simply replaced by a call to
hclust1d
in a plug-and-play manner, with the
surrounding code unchanged. The how-to is covered in detail in our replacing
stats::hclust
vignette
For information on how to get started using hclust1d
,
see our getting
started vignette.
hclust1d
packageTo install the development package version please execute
library(devtools)
devtools::install_github("SzymonNowakowski/hclust1d")
Alternatively, to install the current stable CRAN version please execute
install.packages("hclust1d")
After that, you can load the installed package into memory with a
call to library(hclust1d)
.