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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).
You can install the development version of gmeans from GitHub with:
# install.packages("pak")
pak::pak("m-muecke/gmeans")library(gmeans)
km <- gmeans(mtcars)
km
#> K-means clustering with 2 clusters of sizes 14, 18
#>
#> Cluster means:
#> mpg cyl disp hp drat wt qsec vs
#> 1 15.10000 8.000000 353.1000 209.21429 3.229286 3.999214 16.77214 0.0000000
#> 2 23.97222 4.777778 135.5389 98.05556 3.882222 2.609056 18.68611 0.7777778
#> am gear carb
#> 1 0.1428571 3.285714 3.500000
#> 2 0.6111111 4.000000 2.277778
#>
#> Clustering vector:
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 2 2 2 2
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 1 2 1 2
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 2 2 2 1
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 1 1 1 1
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 1 2 2 2
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 2 1 1 1
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 1 2 2 2
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 1 2 1 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 93643.90 58920.54
#> (between_SS / total_SS = 75.5 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault"Use gmeans() when the number of clusters is unknown and
the clusters are roughly Gaussian. The algorithm splits a cluster only
when an Anderson-Darling test rejects normality, so the number of
clusters follows from the data and a single significance level rather
than from a grid search over k.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.