mlr_measures_clust.ch: Calinski Harabasz Pseudo F-Statistic

In mlr3cluster: Cluster Extension for 'mlr3'

Calinski Harabasz Pseudo F-Statistic

Description

The Calinski-Harabasz index (also known as the Variance Ratio Criterion) is the ratio of between-cluster variance to within-cluster variance, adjusted for the number of clusters and observations. It is defined as CH = \frac{\mathrm{tr}(B) / (k - 1)}{\mathrm{tr}(W) / (n - k)} where B is the between-cluster scatter matrix, W is the within-cluster scatter matrix, k is the number of clusters, and n is the number of observations. Higher values indicate better-defined clusters.

Dictionary

This mlr3::Measure can be instantiated via the dictionary mlr3::mlr_measures or with the associated sugar function mlr3::msr():

mlr_measures$get("clust.ch")
msr("clust.ch")

Meta Information

• Task type: “clust”

• Range: [0, \infty)

• Minimize: FALSE

• Average: macro

• Required Prediction: “partition”

• Required Packages: mlr3, mlr3cluster

References

Caliński, Tadeusz, Harabasz, Jerzy (1974). “A dendrite method for cluster analysis.” Communications in Statistics, 3(1), 1–27. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610927408827101")}.

See Also

Dictionary of Measures: mlr3::mlr_measures

as.data.table(mlr_measures) for a complete table of all (also dynamically created) mlr3::Measure implementations.

Other cluster measures: mlr_measures_clust.avg_between, mlr_measures_clust.avg_within, mlr_measures_clust.davies_bouldin, mlr_measures_clust.dunn, mlr_measures_clust.dunn2, mlr_measures_clust.entropy, mlr_measures_clust.pearsongamma, mlr_measures_clust.silhouette, mlr_measures_clust.wb_ratio, mlr_measures_clust.wss

mlr3cluster documentation built on June 11, 2026, 5:06 p.m.