k_means

<code>k_means()</code> 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.<ul>
<li>stats: Classical K-means</li>
<li>ClusterR: Classical K-means</li>
<li>klaR: K-Modes</li>
<li>clustMixType: K-prototypes</li>
</ul>

A common interface to specifying clustering models, in the
same style as 'parsnip'. Creates unified interface across different
functions and computational engines.

Emil Hvitfeldt

tidyclust

A Common API to Clustering

Kelly Bodwin

Posit Software, PBC 

k_means function

<dl><dt>mode</dt>
<dd>A single character string for the type of model. The only
possible value for this model is "partition".</dd>
<dt>engine</dt>
<dd>A single character string specifying what computational engine
to use for fitting. Possible engines are listed below. The default for this
model is <code>"stats"</code>.</dd>
<dt>num_clusters</dt>
<dd>Positive integer, number of clusters in model.</dd></dl>

Arguments

K-Means — k_means

<dl>

<dt>mode</dt>
<dd>A single character string for the type of model. The only
possible value for this model is "partition".</dd>


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


<dt>num_clusters</dt>
<dd>Positive integer, number of clusters in model.</dd>

</dl>

k_means: K-Means

Description

Usage

Value

Arguments

Details

What does it mean to predict?

Examples