ClusterInterDistances: Computes Inter-Cluster Distances

In FCPS: Fundamental Clustering Problems Suite

Computes Inter-Cluster Distances

Description

Computes inter-cluster distances which are the distance between each cluster and all other clusters

Usage

ClusterInterDistances(FullDistanceMatrix, Cls,

Names,PlotIt=FALSE)

Arguments

FullDistanceMatrix	[1:n,1:n] symmetric distance matrix
Cls	[1:n] numerical vector of numbers defining the classification as the main output of the clustering algorithm for the n cases of data. It has k unique numbers representing the arbitrary labels of the clustering.
Names	Optional [1:k] character vector naming k classes
PlotIt	Optional, Plots if TRUE

Details

Cluster distances are given back as a matrix, one column per cluster and the vector of the full distance matrix without the diagonal elements and the upper half of the symmetric matrix. Details and definitons can be found in [Thrun, 2021].

Value

Matrix [1:m,1:(k+1)] of k clusters, each columns consists of the distances between a cluster and all other clusters, filled up with NaN at the end to be of the same lenght as the vector of the upper triangle of the complete distance matrix.

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

ClusterDistances

Examples

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

interdists=ClusterInterDistances(Distance,Hepta$Cls)

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.