FitKMeans: Fit a series of kmeans clusterings and compute Hartigan's...

In useful: A Collection of Handy, Useful Functions

Fit a series of kmeans clusterings and compute Hartigan's Number

Description

Given a numeric dataset this function fits a series of kmeans clusterings with increasing number of centers. k-means is compared to k+1-means using Hartigan's Number to determine if the k+1st cluster should be added.

Usage

FitKMeans(x, max.clusters = 12L, spectral = FALSE, nstart = 1L,
  iter.max = 10L, algorithm = c("Hartigan-Wong", "Lloyd", "Forgy",
  "MacQueen"), seed = NULL)

Arguments

x	The data, numeric, either a matrix or data.frame
max.clusters	The maximum number of clusters that should be tried
spectral	logical; If the data being fit are eigenvectors for spectral clustering
nstart	The number of random starts for the kmeans algorithm to use
iter.max	Maximum number of tries before the kmeans algorithm gives up on conversion
algorithm	The desired algorithm to be used for kmeans. Options are c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"). See kmeans
seed	If not null, the random seed will be reset before each application of the kmeans algorithm

Details

A consecutive series of kmeans is computed with increasing k (number of centers). Each result for k and k+1 are compared using Hartigan's Number. If the number is greater than 10, it is noted that having k+1 clusters is of value.

Value

A data.frame consisting of columns, for the number of clusters, the Hartigan Number and whether that cluster should be added, based on Hartigan's Number.

Author(s)

Jared P. Lander www.jaredlander.com

References

http://www.stat.columbia.edu/~madigan/DM08/descriptive.ppt.pdf

See Also

kmeans PlotHartigan

Examples

data(iris)
hartiganResults <- FitKMeans(iris[, -ncol(iris)])
PlotHartigan(hartiganResults)

useful documentation built on Oct. 24, 2023, 9:07 a.m.