mlr_measures_clust.dunn: Dunn Index

In mlr3cluster: Cluster Extension for 'mlr3'

Dunn Index

Description

The Dunn index is the ratio of the smallest inter-cluster distance to the largest intra-cluster diameter, defined as D = \min_{i \neq j} \delta(C_i, C_j) / \max_k \Delta(C_k) where \delta(C_i, C_j) is the minimum distance between clusters i and j, and \Delta(C_k) is the maximum distance between any two observations in cluster k. Higher values indicate compact, well-separated clusters.

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.dunn")
msr("clust.dunn")

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.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.