visCPS: CPS Analysis on selecting visualization method.

In OTclust: Mean Partition, Uncertainty Assessment, Cluster Validation and Visualization Selection for Cluster Analysis

CPS Analysis on selecting visualization method.

Description

Covering Point Set Analysis on the visualization results. Use K-Nearest Neighbor to generate a collection of results for CPS Analysis. The return contains several statistics which can be directly used as input for mplot or cplot.

Usage

visCPS(vlab, ref, nEXP = 100)

Arguments

vlab	– the coordinates generated by one visualization method in a numeric matrix of two columns.
ref	– the true labels in a vector format, the first cluster is labeled as 1.
nEXP	– number of perturbed results for CPS Analysis.

Value

a list used for mplot or cplot, in which tight_all is the overall tightness, member is the matrix used for the membership heat map, set is the matrix for the covering point set plot, tight is the vector of cluster-wise tightness, vis is the visualization coordinates, ref is the reference labels and topo is the topological relationship between clusters for point-wise uncertainty assessment.

Examples

# CPS analysis on selection of visualization methods
data(vis_pollen)
c=visCPS(vis_pollen$vis, vis_pollen$ref)
# visualization of the results
mplot(c,2)
cplot(c,2)

OTclust documentation built on Oct. 6, 2023, 5:09 p.m.