ContTrustMeasure {ProjectionBasedClustering}

continuity and trustworthiness

Description

Computes trustworthiness and continuity for projected data (see [Kaski2003]).

Usage

ContTrustMeasure(datamat, projmat, lastNeighbor)

Arguments

datamat

numerical matrix of data: n cases in rows, d variables in columns

projmat

numerical matrix of projected data: n cases in rows, k variables in columns, where k is the projection output dimension

lastNeighbor

scalar, maximal size of neighborhood to be considered

Details

This is a wrapper that is used in the DRquality to investigate varius quality measurements [Thrun et al, 2023]. The paper indicates, that the Gabriel classification error seems to be a good alternative. [Thrun et al, 2023].

Value

numerical [k,7] matrix, where k is the lastNeighbor value. The matrix contains the columns:

Neighborhood size; worst-case trustworthiness; average trustworthiness; best-case trustworthiness; worst-case continuity; average continuity; best-case continuity

where neighborhood size is the size of the neighberhood considered, which ranges from 1:lastNeighbor

Note

C++ source code comes from https://research.cs.aalto.fi/pml/software/dredviz/

Author(s)

Luca Brinkmann, Felix Pape

References

[Kaski2003]: Samuel Kaski, Janne Nikkilä, Merja Oja, Jarkko Venna, Petri Törönen, and Eero Castren. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinformatics, 4:48, 2003.

See Also

For plotting see plotMeasureTundD in the package DRquality. An alternative measure is the KLMeasure, see also GabrielClassificationError

Examples

data('Hepta')
Data=Hepta$Data
res=MDS(Data)
Proj = res$ProjectedPoints
ContTrustMeasure(Hepta$Data, Proj, 10)