trainstepC

array (1:Lines,1:Columns,1:Weights), WeightVectors that will be trained, internally transformed von NumericVector to cube

array (1:Lines,1:Columns,1:2), meshgrid for output distance computation

NumericMatrix, n cases shuffled Dataset[1:n,1:d] by <code>sample</code>

DataSampled

NumericMatrix, n cases shuffled BestMatches[1:n,1:2] by <code>sample</code> in the same way as <code>DataSampled</code>

BMUsampled

Lines

Columns

double, The current Radius that should be used to define neighbours to the bm

Radius

bool, Should the grid be considered with cyclically connected borders?

toroid

Does the training for fixed bestmatches in one epoch of the sESOM.

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.

trainstepC: internal function for s-esom

Description

Usage

Arguments

Value

Details

References