NormalizeUmatrix: Normalize Umatrix

In GeneralizedUmatrix: Credible Visualization for Two-Dimensional Projections of Data

Normalize Umatrix

Description

Normalizing the U-matrix using the abstact U-Matrix concept [Loetsch/Ultsch, 2014].

Usage

NormalizeUmatrix(Data, Umatrix, BestMatches)

Arguments

Data	[1:n,1:d] numerical matrix of data with n cases and d variables
Umatrix	[1:lines,1:Columns] matrix of U-heights
BestMatches	[1:n,1:2] Bestmatching units.

Details

see publication [Loetsch/Ultsch, 2014]..

Value

Normalized Umatrix[1:lines,1:Columns] using the abstact U-Matrix concept.

Author(s)

Felix Pape, Michael Thrun

References

Loetsch, J., Ultsch, A.: Exploiting the structures of the U-matrix, in Villmann, T., Schleif, F.-M., Kaden, M. & Lange, M. (eds.), Proc. Advances in Self-Organizing Maps and Learning Vector Quantization, pp. 249-257, Springer International Publishing, Mittweida, Germany, 2014.

Examples

data("Chainlink")
Data=Chainlink$Data
Cls=Chainlink$Cls
InputDistances=as.matrix(dist(Data))
res=cmdscale(d=InputDistances, k = 2, eig = TRUE, add = FALSE, x.ret = FALSE)
ProjectedPoints=as.matrix(res$points)
#see also ProjectionBasedClustering package for other common projection methods


  resUmatrix=GeneralizedUmatrix(Data,ProjectedPoints)
  ## Normalization
  normalizedUmatrix=NormalizeUmatrix(Data,resUmatrix$Umatrix,resUmatrix$Bestmatches)
  ## visualization
  TopviewTopographicMap(GeneralizedUmatrix = normalizedUmatrix,resUmatrix$Bestmatches)

GeneralizedUmatrix documentation built on May 31, 2023, 7:26 p.m.