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K-Means

Source: R/k_means.R
k_means.Rd

k_means() defines a model that fits clusters based on distances to a number of centers. This definition doesn't just include K-means, but includes models like K-prototypes.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

Usage

k_means(mode = "partition", engine = "stats", num_clusters = NULL)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "partition".

engine

A single character string specifying what computational engine to use for fitting. Possible engines are listed below. The default for this model is "stats".

num_clusters

Positive integer, number of clusters in model.

Value

A k_means cluster specification.

Details

What does it mean to predict?

For a K-means model, each cluster is defined by a location in the predictor space. Therefore, prediction in tidyclust is defined by calculating which cluster centroid an observation is closest too.

Examples

# Show all engines
modelenv::get_from_env("k_means")
#> # A tibble: 4 × 2
#>   engine       mode     
#>   <chr>        <chr>    
#> 1 stats        partition
#> 2 ClusterR     partition
#> 3 clustMixType partition
#> 4 klaR         partition

k_means()
#> K Means Cluster Specification (partition)
#> 
#> Computational engine: stats 
#>