`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.

stats: Classical K-means

ClusterR: Classical K-means

klaR: K-Modes

clustMixType: K-prototypes

## 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.

## 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
#>
```