ClusterDistances: ClusterDistances

In FCPS: Fundamental Clustering Problems Suite

ClusterDistances

Description

Computes the within-cluster distances for each cluster.

Usage

ClusterDistances(FullDistanceMatrix, Cls,

Names, PlotIt = FALSE)

ClusterIntraDistances(FullDistanceMatrix, Cls,

Names, PlotIt = FALSE)

Arguments

FullDistanceMatrix	[1:n,1:n] symmetric distance matrix
Cls	[1:n] numerical vector of k classes
Names	Optional [1:k] character vector naming k classes
PlotIt	Optional, Plots if TRUE

Details

Returns a matrix with one column per cluster and one column containing all pairwise distances from the upper triangle of the complete distance matrix. Details and definitions can be found in [Thrun, 2021].

Value

A matrix with k + 1 columns. The first column contains all pairwise distances; each remaining column contains the within-cluster distances for one cluster. Shorter columns are padded with NaN; a singleton cluster therefore has NaN as its within-cluster distance.

Author(s)

Michael Thrun

References

[Thrun, 2021] Thrun, M. C.: The Exploitation of Distance Distributions for Clustering, International Journal of Computational Intelligence and Applications, Vol. 20(3), pp. 2150016, DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1142/S1469026821500164")}, 2021.

See Also

MDplot

ClusterInterDistances

Examples

data(Hepta)
Distance=as.matrix(dist(Hepta$Data))

interdists=ClusterDistances(Distance,Hepta$Cls)

FCPS documentation built on Oct. 3, 2026, 9:06 a.m.