ContTrustMeasure: Measure of trustworthiness and continuity for projection

In ProjectionBasedClustering: Projection Based Clustering

Measure of trustworthiness and continuity for projection

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

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

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

Author(s)

Michael Thrun

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

An alternative measure is the KLMeasure

Examples

data('Hepta')
Data=Hepta$Data
res=MDS(Data)
Proj = res$ProjectedPoints
PlotProjectedPoints(res$ProjectedPoints,Hepta$Cls)

ContTrustMeasure(Hepta$Data, Proj, 10)

ProjectionBasedClustering documentation built on Oct. 12, 2023, 1:07 a.m.