mlr_measures_clust.entropy: Entropy

In mlr3cluster: Cluster Extension for 'mlr3'

Entropy

Description

The Shannon entropy of the cluster size distribution, defined as H = -\sum_{k=1}^{K} p_k \log(p_k) where p_k = n_k / n is the proportion of observations in cluster k. Lower values indicate more uneven cluster sizes (with 0 for a single cluster), while higher values indicate more uniform sizes. This measure does not evaluate cluster quality directly but characterizes the balance of the partition.

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.entropy")
msr("clust.entropy")

Meta Information

• Task type: “clust”

• Range: [0, \infty)

• Minimize: NA

• Average: macro

• Required Prediction: “partition”

• Required Packages: mlr3, mlr3cluster

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.dunn2, 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.