iEsomTrain

Trains the ESOM and shows the Umatrix.

By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data.

Jorn Lotsch

Umatrix

Visualization of Structures in High-Dimensional Data

Florian Lerch

Michael Thrun

Felix Pape

Raphael Paebst

Alfred Ultsch

iEsomTrain function

<dl> <dt>Data</dt>
<dd>Matrix of Data that will be used to learn. One DataPoint per row</dd> <dt>BestMatches</dt>
<dd>Array with positions of Bestmatches</dd> <dt>Cls</dt>
<dd>Classification of the Bestmatches as a vector</dd> <dt>Key</dt>
<dd>Numeric vector of keys matching the Bestmatches</dd> <dt>Toroid</dt>
<dd>Are BestMatches placed on a toroid grid? TRUE by default</dd></dl>

Arguments

iEsomTrain — iEsomTrain

<dl>

 <dt>Data</dt>
<dd>Matrix of Data that will be used to learn. One DataPoint per row</dd>

 <dt>BestMatches</dt>
<dd>Array with positions of Bestmatches</dd>

 <dt>Cls</dt>
<dd>Classification of the Bestmatches as a vector</dd>

 <dt>Key</dt>
<dd>Numeric vector of keys matching the Bestmatches</dd>

 <dt>Toroid</dt>
<dd>Are BestMatches placed on a toroid grid? TRUE by default</dd>

</dl>

iEsomTrain: iEsomTrain

Description

Value

Arguments

References