mlr_measures_clust.dunn2: Dunn2 Index

In mlr3cluster: Cluster Extension for 'mlr3'

Dunn2 Index

Description

An alternative formulation of the Dunn index that uses average distances instead of extremes. It is defined as the ratio of the minimum average between-cluster distance to the maximum average within-cluster distance: D_2 = \min_{i \neq j} \bar{d}(C_i, C_j) / \max_k \bar{d}(C_k). This variant is more robust to outliers than the standard Dunn index. Higher values indicate better separation.

Details

If the task contains factor or ordered features, Gower distances (cluster::daisy()) are used instead of Euclidean distances.

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.dunn2")
msr("clust.dunn2")

Meta Information

• Task type: “clust”

• Range: [0, \infty)

• Minimize: FALSE

• Average: macro

• Required Prediction: “partition”

• Required Packages: mlr3, mlr3cluster, cluster

References

Dunn, C J (1974). “Well-separated clusters and optimal fuzzy partitions.” Journal of Cybernetics, 4(1), 95–104. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01969727408546059")}.

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.ch, mlr_measures_clust.davies_bouldin, mlr_measures_clust.dunn, 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.