README.md

In kosugitti/imKmeans: Improved k-means clustering function

imKmeans

Improved k-means method, developed by Toyoda and Ikehara(2011), uses mahalanobis distance as similarity between variables. And by using this method, we can determine the number of clusters with AIC and BIC.

Install

You need devtools to install imKmeans from GitHub.

install.packages("devtools")

After install these packages, excute the following in R:

library(devtools)
install_github("kosugitti/imKmeans")

Example

imKmeans(iris[1:4],4)

Reference

Toyoda H. and Ikehara K. 2011 An improved method using k-means to determine the optimal number of clusters, considering the relations between several variables. The Japanese Journal of Psychology,Vol.82, No.1, pp.32-40.

Note

AIC and BIC yielded by this function are differ from the result of Toyoda et al.(2011). because of counting the number of free parameters...

kosugitti/imKmeans documentation built on May 20, 2019, 1:08 p.m.