ClusterMCC: Matthews Correlation Coefficient (MCC)

In FCPS: Fundamental Clustering Problems Suite

Matthews Correlation Coefficient (MCC)

Description

Matthews correlation coefficient eneralized to the multiclass case (a.k.a. R_K statistic).

Usage

ClusterMCC(PriorCls, CurrentCls,Force=TRUE)

Arguments

PriorCls	Ground truth,[1:n] numerical vector with n numbers defining the classification. It has k unique numbers representing the 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 labels of the clustering.
Force	Logical scalar. If TRUE, forces computation when one or more labels in PriorCls are absent from CurrentCls, or vice versa. One placeholder observation is added for each missing label.

Details

Contrary to accuracy, the MCC is balanced measure which can be used even if the classes are of very different sizes. When there are more than two labels the MCC will no longer range between -1 and +1. Instead the minimum value will be between -1 and 0 depending on the true distribution. The maximum value is always +1. Beware that in contrast to ClusterAccuracy, the labels cannot be arbitrary. Instead each label of PriorCls and CurrentCls has to be mapped to the same cluster of data points. Typically this has to be ensured manually.

Value

A numeric MCC scalar in the range described in Details.

Note

If the numbers of clusters differ, missing clusters are aligned internally using placeholder observations.

Author(s)

Michael Thrun

References

Matthews, B. W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochimica et Biophysica Acta (BBA), Protein Structure, Vol. 405(2), pp. 442-451, 1975.

Boughorbel, S.B: Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric, PLOS ONE, Vol. 12(6), pp. e0177678, 2017.

Chicco, D.; Toetsch, N. and Jurman, G.: The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two_class confusion matrix evaluation. BioData Mining. Vol. 14., 2021.

See Also

ClusterAccuracy

Examples

#Beware that algorithm arbitrary defines the labels
data(Hepta)
V=kmeansClustering(Hepta$Data,Type = "Hartigan",7)
table(V$Cls,Hepta$Cls)
#result is only valid if the above issue is resolved manually
ClusterMCC(Hepta$Cls,V$Cls)

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