The aim of the package is to provide an implementation of the G-means algorithm in R. The G-means algorithm is a clustering algorithm that extends the k-means algorithm by automatically determining the number of clusters. The algorithm was introduced by Hamerly and Elkan (2003).
Installation
You can install the development version of gmeans from GitHub with:
# install.packages("pak")
pak::pak("m-muecke/gmeans")Usage
library(gmeans)
km <- gmeans(mtcars)
km
#> K-means clustering with 2 clusters of sizes 18, 14
#>
#> Cluster means:
#> mpg cyl disp hp drat wt qsec vs
#> 1 23.97222 4.777778 135.5389 98.05556 3.882222 2.609056 18.68611 0.7777778
#> 2 15.10000 8.000000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
#> am gear carb
#> 1 0.6111111 4.000000 2.277778
#> 2 0.1428571 3.285714 3.500000
#>
#> Clustering vector:
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 1 1 1 1
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 2 1 2 1
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 1 1 1 2
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 2 2 2 2
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 2 1 1 1
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 1 2 2 2
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 2 1 1 1
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 2 1 2 1
#>
#> Within cluster sum of squares by cluster:
#> [1] 58920.54 93643.90
#> (between_SS / total_SS = 75.5 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"Related work
- nortest: R package for testing the composite hypothesis of normality.