ClusterChallenge: Generates a Fundamental Clustering Challenge based on...

In Mthrun/FCPS: Fundamental Clustering Problems Suite

Generates a Fundamental Clustering Challenge based on specific artificial datasets.

Description

Lsun3D and FCPS datasets were introduced in various publications for a specific fixed size. This function generalizes them for any sample size.

Usage

ClusterChallenge(Name,SampleSize,

PlotIt=FALSE,PointSize=1,Plotter3D="rgl",...)

Arguments

Name	string, either 'Atom', 'Chainlink, 'EngyTime', 'GolfBall', 'Hepta', 'Lsun3D', 'Target' 'Tetra' 'TwoDiamonds' 'WingNut
SampleSize	Size of Sample higher than 300, preferable above 500
PlotIt	TRUE: Plots the challenge with ClusterPlotMDS
PointSize	If PlotIt=TRUE: see ClusterPlotMDS
Plotter3D	If PlotIt=TRUE: see ClusterPlotMDS
...	If PlotIt=TRUE: further arguments for ClusterPlotMDS

Details

A detailed description of the datasets can be found in [Thrun/Ultsch 2020]. Sampling works by combining Pareto Density Estimation with rejection sampling.

Value

LIST, with

Name	[1:SampleSize,1:d] data matrix
Cls	[1:SampleSize] numerical vector of classification

Author(s)

Michael Thrun

References

[Thrun/Ultsch, 2020] Thrun, M. C., & Ultsch, A.: Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems, Data in Brief, Vol. in press, pp. 105501, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.dib.2020.105501")}, 2020.

See Also

ClusterPlotMDS

Examples

## Not run: 
ClusterChallenge("Chainlink",2000,PlotIt=TRUE)

## End(Not run)

Mthrun/FCPS documentation built on June 28, 2023, 9:29 a.m.