GeneralizedUmatrixGPU (version 0.1.14)

GeneralizedUmatrixGPU-package: tools:::Rd_package_title("GeneralizedUmatrixGPU")

Description

tools:::Rd_package_description("GeneralizedUmatrixGPU")

Author

Quirin Stier

Details

For a brief introduction to GeneralizedUmatrixGPU please see the vignette of the CRAN package GeneralizedUmatrix.

For further details regarding the generalized Umatrix see [Thrun, 2018], chapter 4-5, or [Thrun/Ultsch, 2020].

If you want to verifiy your clustering result externally, you can use Heatmap or SilhouettePlot of the CRAN package DataVisualizations.

tools:::Rd_package_indices("GeneralizedUmatrixGPU")

References

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Uncovering High-Dimensional Structures of Projections from Dimensionality Reduction Methods, MethodsX, Vol. 7, pp. 101093, DOI tools:::Rd_expr_doi("10.1016/j.mex.2020.101093"), 2020.

[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.

[Ultsch/Thrun, 2017] Ultsch, A., & Thrun, M. C.: Credible Visualizations for Planar Projections, in Cottrell, M. (Ed.), 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), IEEE Xplore, France, 2017.

Examples

data("Chainlink")
SampleIdx = sample(1:1000, 200)
Data=Chainlink$Data[SampleIdx,]
Cls=Chainlink$Cls[SampleIdx]
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 R package 'ProjectionBasedClustering' for other common projection methods
# see DatabionicSwarm for projection method without parameters or objective function
# ProjectedPoints=DatabionicSwarm::Pswarm(Data)$ProjectedPoints
# \donttest{
resUmatrix=GeneralizedUmatrixGPU(Data,ProjectedPoints)
#library(GeneralizedUmatrix)
#plotTopographicMap(resUmatrix$Umatrix,resUmatrix$Bestmatches,Cls)
# }