kMeansMap

May give better results than 'RandomMap' on data where random sampling
is complicated.
This does not use actual kMeans clustering, but re-uses the batch version of
<code>SOM()</code> with tiny radius (which makes it work the same as kMeans). In
consequence, the speedup of SOM function is applied here as well. Additionally,
because we don't need that amount of clustering precision, parameters `batch=F, rlen=1'
may give a satisfactory result very quickly.

Provides a smooth mapping of multidimensional points into
low-dimensional space defined by a self-organizing map. Designed to work
with 'FlowSOM' and flow-cytometry use-cases. See Kratochvil et al. (2019)
<doi:10.12688/f1000research.21642.1>.

Mirek Kratochvil

EmbedSOM

Fast Embedding Guided by Self-Organizing Map

Sofie Van Gassen

Britt Callebaut

Yvan Saeys

Ron Wehrens

kMeansMap function

<dl><dt>data</dt>
<dd>Input data matrix, with individual data points in rows</dd>
<dt>k</dt>
<dd>How many points to sample</dd>
<dt>coordsFn</dt>
<dd>a function to generate embedding coordinates (default none)</dd>
<dt>batch</dt>
<dd>Use batch-SOM training (effectively kMeans, default TRUE)</dd>
<dt>...</dt>
<dd>Passed to <code>SOM()</code>, useful e.g. for 'parallel=T' or 'rlen=5'</dd></dl>

Arguments

Create a map from k-Means clusters — kMeansMap

<dl>

<dt>data</dt>
<dd>Input data matrix, with individual data points in rows</dd>


<dt>k</dt>
<dd>How many points to sample</dd>


<dt>coordsFn</dt>
<dd>a function to generate embedding coordinates (default none)</dd>


<dt>batch</dt>
<dd>Use batch-SOM training (effectively kMeans, default TRUE)</dd>


<dt>...</dt>
<dd>Passed to <code>SOM()</code>, useful e.g. for 'parallel=T' or 'rlen=5'</dd>

</dl>

kMeansMap: Create a map from k-Means clusters

Description

Usage

Value

Arguments

Examples