ClusterRedefine: Redfines Clustering

In FCPS: Fundamental Clustering Problems Suite

Redfines Clustering

Description

Redfines some or all Clusters of Clustering such that the names of the numerical vectors are defined by

Usage

ClusterRedefine(Cls, NewLabels,OldLabels)

Arguments

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.
NewLabels	[1:p], p<=k labels (identifiers) of clusters to be changed with
OldLabels	Optional, [1:p], p<=k labels(identifiers) of clusters to be changed, default [1:k] unique cluster Ids of Cls

Details

The same ordering of NewLabels and OldLabels is assumend, i.e., the mapping is defined by OldLabels[i] -> NewLabels[i] with i in [1:p]. NewLabels can also be a vector for strings, for example for plotting.

Value

Cls[1:n] numerical vector named after the row names of data

Author(s)

Michael Thrun

Examples

data('Lsun3D')
Cls=Lsun3D$Cls
Data=Lsun3D$Data#
#prior
ClsNew=unique(Cls)+10
#Redfined Clustering
NewCls=ClusterRedefine(Cls,ClsNew)

table(Cls,NewCls)

#require(DataVisualizations)
n=length(unique(Cls))
NewCls=ClusterRedefine(Cls,LETTERS[1:n])
#DataVisualizations package required
if(requireNamespace("DataVisualizations"))
  DataVisualizations::Classplot(Data[,1],Data[,2],
  Cls,Names=NewCls,Plotter="ggplot",Size =1.5)

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