find_algorithm

kmean_assignment_c

kmean_assignment_r

<code>find_algorithm()</code> returns a function to assign k-means cluster.
<code>kmean_assignment_r()</code> wraps around <code><a href="/link/kmeans()?package=partition&version=0.2.2" data-mini-rdoc="partition::kmeans()">kmeans()</a></code> to pull the correct
assignments.

internal

A fast and flexible framework for agglomerative partitioning.
'partition' uses an approach called Direct-Measure-Reduce to create
new variables that maintain the user-specified minimum level of
information. Each reduced variable is also interpretable: the original
variables map to one and only one variable in the reduced data set.
'partition' is flexible, as well: how variables are selected to
reduce, how information loss is measured, and the way data is reduced
can all be customized. 'partition' is based on the Partition
framework discussed in Millstein et al. (2020)
<doi:10.1093/bioinformatics/btz661>.

Malcolm Barrett

partition

Agglomerative Partitioning Framework for Dimension Reduction

Joshua Millstein

Katelyn Queen

find_algorithm function

<dl><dt>algorithm</dt>
<dd>the kmeans algorithm to use</dd></dl>

Arguments

<code>find_algorithm()</code> returns a function to assign k-means cluster.
<code>kmean_assignment_r()</code> wraps around <code><a href='https://rdrr.io/r/stats/kmeans.html'>kmeans()</a></code> to pull the correct
assignments.

Which kmeans algorithm to use? — find_algorithm

<dl>

<dt>algorithm</dt>
<dd>the kmeans algorithm to use</dd>

</dl>

find_algorithm: Which kmeans algorithm to use?

Description

Usage

Value

Arguments