ProjectionBasedClustering (version 1.2.1)

interactiveClustering: GUI for interactive cluster analysis

Description

This tool is an interactive shiny tool that visualizes a given generalized Umatrix and allows the user to select areas and mark them as clusters to improve a projection based clustering.

Value

List of

EnlargedUmatrix

[1:Lines,1:Columns] Matrix of Umatrix Heights taken four times and arranged in a square 2x2.

EnlargedBestmaches

[1:n,1:2] Array with positions of Bestmatches according to the enlarged umatrix.

EnlargedCls

[1:n] Classification of the Bestmatches according to the enlarged umatrix.

Umatrix

[1:Lines,1:Columns] Matrix of Umatrix Heights

Bestmaches

[1:n,1:2] Array with positions of Bestmatches

Cls

[1:n] Classification of the Bestmatches

TopView_TopographicMap

Plot of a topographic map.

Arguments

Umatrix

[1:Lines,1:Columns] Matrix of Umatrix Heights

Bestmaches

[1:n,1:2] Array with positions of Bestmatches

Cls

[1:n] Classification of the Bestmatches

Author

Florian Lerch, Michael Thrun

Details

Clicking on "Quit" returns the Cls vector to the workspace.

References

[Thrun/Ultsch, 2017] Thrun, M.C., Ultsch, A.: Projection based Clustering, Conf. Int. Federation of Classification Societies (IFCS),DOI:10.13140/RG.2.2.13124.53124, Tokyo, 2017.

[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, tools:::Rd_expr_doi("10.1007/978-3-658-20540-9"), 2018.

Examples

data('Hepta')
#2d projection
# Visualizuation of GeneralizedUmatrix
# \donttest{
projectionpoints=NeRV(Hepta$Data)
#Computation of Generalized Umatrix
library(GeneralizedUmatrix)
visualization=GeneralizedUmatrix(Data = Hepta$Data,projectionpoints)
# }
## Semi-Automatic Clustering done interactivly in a  shiny gui
if (FALSE) {
Cls = interactiveClustering(visualization$Umatrix, visualization$Bestmatches)
##Plotting
plotTopographicMap(visualization$Umatrix,visualization$Bestmatches,Cls)
}