ClusterAccuracy: ClusterAccuracy

In FCPS: Fundamental Clustering Problems Suite

ClusterAccuracy

Description

ClusterAccuracy

Usage

ClusterAccuracy(PriorCls,CurrentCls,K=9)

Arguments

PriorCls	Ground truth,[1:n] numerical vector with n numbers defining the classification. It has k unique numbers representing the arbitrary labels of the clustering.
CurrentCls	Main output of the clustering, [1:n] numerical vector with n numbers defining the classification. It has k unique numbers representing the arbitrary labels of the clustering.
K	Maximal number of classes for computation.

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 [Thrun et al., 2018]. Beware that in contrast to ClusterMCC, the labels can be arbitrary. However, accuracy is a only a valid quality measure if the clusters are balanced (of) nearly equal size). Ohterwise please use ClusterMCC.

In contrast to the F-measure, "Accuracy tends to be naturally unbiased, because it can be expressed in terms of a binomial distribution: A success in the underlying Bernoulli trial would be defined as sampling an example for which a classifier under consideration makes the right prediction. By definition, the success probability is identical to the accuracy of the classifier. The i.i.d. assumption implies that each example of the test set is sampled independently, so the expected fraction of correctly classified samples is identical to the probability of seeing a success above. Averaging over multiple folds is identical to increasing the number of repetitions of the Binomial trial. This does not affect the posterior distribution of accuracy if the test sets are of equal size, or if we weight each estimate by the size of each test set." [Forman/Scholz, 2010]

Value

Single scalar of Accuracy between zero and one

Author(s)

Michael Thrun

References

[Thrun et al., 2018] 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

[Forman/Scholz, 2010] Forman, G., and Scholz, M.: Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement, ACM SIGKDD Explorations Newsletter, Vol. 12(1), pp. 49-57. 2010.

See Also

ClusterMCC

Examples

#Influence of random sets/ random starts on k-means

data('Hepta')
Cls=kmeansClustering(Hepta$Data,7,Type = "Hartigan",nstart=1)
table(Cls$Cls,Hepta$Cls)
ClusterAccuracy(Hepta$Cls,Cls$Cls)



data('Hepta')
Cls=kmeansClustering(Hepta$Data,7,Type = "Hartigan",nstart=100)
table(Cls$Cls,Hepta$Cls)
ClusterAccuracy(Hepta$Cls,Cls$Cls)

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