KMeansPP: k-means++ clustering

In TreeDist: Calculate and Map Distances Between Phylogenetic Trees

k-means++ clustering

Description

k-means++ clustering \insertCiteArthur2007TreeDist improves the speed and accuracy of standard kmeans clustering \insertCiteHartigan1979TreeDist by preferring initial cluster centres that are far from others. A scalable version of the algorithm has been proposed for larger data sets \insertCiteBahmani2012TreeDist, but is not implemented here.

Usage

KMeansPP(x, k = 2, nstart = 10, ...)

Arguments

x	Numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).
k	Integer specifying the number of clusters, k.
nstart	Positive integer specifying how many random sets should be chosen
...	additional arguments passed to kmeans

Author(s)

Martin R. Smith ([e-mail])

References

\insertAllCited

See Also

kmeans

Other cluster functions: cluster-statistics

Examples

# Generate random points
set.seed(1)
x <- cbind(c(rnorm(10, -5), rnorm(5, 1), rnorm(10, 6)),
           c(rnorm(5, 0), rnorm(15, 4), rnorm(5, 0)))

# Conventional k-means may perform poorly
klusters <- kmeans(x, cent = 5)
plot(x, col = klusters$cluster, pch = rep(15:19, each = 5))

# Here, k-means++ recovers a better clustering
plusters <- KMeansPP(x, k = 5)
plot(x, col = plusters$cluster, pch = rep(15:19, each = 5))

TreeDist documentation built on Oct. 26, 2023, 1:07 a.m.