k_means: K-Means

In tidyclust: A Common API to Clustering

K-Means

Description

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

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.

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.

Value

A k_means cluster specification.

Examples

# Show all engines
modelenv::get_from_env("k_means")

k_means()

tidyclust documentation built on Sept. 26, 2023, 1:08 a.m.