DatabionicSwarm (version 1.1.0)

ClusteringAccuracy: ClusteringAccuracy

Description

ClusteringAccuracy

Usage

ClusteringAccuracy(PriorCls,CurrentCls,K=9)

Arguments

PriorCls

CurrentCls

clustering result

K

Maximal number of classes for computation.

Value

Accuracy Between zero and one

Details

Here, accuracy is defined as the normalized sum over all true positive labeled data points of a clustering algorithm. The best of all permutation of labels with the highest accuracy is selected in every trial because algorithms arbitrarily define the labels.

References

Michael C. Thrun, Felix Pape, Alfred Ultsch: Benchmarking Cluster Analysis Methods in the Case of Distance and Density-based Structures Defined by a Prior Classification Using PDE-Optimized Violin Plots, ECDA, Potsdam, 2018

Examples

# NOT RUN {
data(Hepta)
# }
# NOT RUN {
InputDistances=as.matrix(dist(Hepta$Data))
projection=Pswarm(InputDistances)
visualization=GeneratePswarmVisualization(Data = Hepta$Data,

projection$ProjectedPoints,projection$LC)
Cls=DBSclustering(k=7, Hepta$Data, visualization$Bestmatches,

visualization$LC,PlotIt=FALSE)
ClusteringAccuracy(Hepta$Cls,Cls,K=9)
# }