sESOM4BMUs: Intern function: Simplified Emergent Self-Organizing Map

In DatabionicSwarm: Swarm Intelligence for Self-Organized Clustering

Intern function: Simplified Emergent Self-Organizing Map

Description

Intern function for the simplified ESOM (sESOM) algorithm for fixed BestMatchingUnits.

Usage

sESOM4BMUs(BMUs,Data, esom, toroid, CurrentRadius,

ComputeInR=FALSE,Parallel=TRUE)

Arguments

BMUs	[1:Lines,1:Columns], BestMAtchingUnits generated by ProjectedPoints2Grid()
Data	[1:n,1:d] array of data: n cases in rows, d variables in columns
esom	[1:Lines,1:Columns,1:weights] array of NeuronWeights, see ListAsEsomNeurons()
toroid	TRUE/FALSE - topology of points
CurrentRadius	number betweeen 1 to x
ComputeInR	=TRUE: Rcode, =FALSE: Cpp
Parallel	Optional, =TRUE: Parallel C++ implementation, =FALSE C++ implementation

Details

Algorithm is described in [Thrun, 2018, p. 48, Listing 5.1].

Value

esom	numeric array [1:Lines,1:Columns,1:d], d is the dimension of the weights, the same as in the ESOM algorithm. modified esomneuros regarding a predefined neighborhood defined by a radius

Note

Usually not for seperated usage!

Author(s)

Michael Thrun

References

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-658-20540-9")}, 2018.

See Also

GeneratePswarmVisualization

DatabionicSwarm documentation built on Oct. 13, 2023, 5:10 p.m.