README.md

In mlr3cluster: Cluster Extension for 'mlr3'

mlr3cluster

Package website: release \| dev

Cluster analysis for mlr3.

mlr3cluster is an extension package for cluster analysis within the mlr3 ecosystem. It is a successor of clustering capabilities of mlr2.

Installation

Install the last release from CRAN:

install.packages("mlr3cluster")

Install the development version from GitHub:

# install.packages("pak")
pak::pak("mlr-org/mlr3cluster")

Feature Overview

The current version of mlr3cluster contains:

• A selection of 39 clustering learners that represent a wide variety of clusterers: partitional, hierarchical, fuzzy, etc.
• A selection of 12 performance measures
• Two built-in tasks to get started with clustering

Also, the package is integrated with mlr3viz which enables you to create great visualizations with just one line of code!

Cluster Analysis

Cluster Learners

| Key | Label | Packages | |:---|:---|:---| | clust.MBatchKMeans | Mini Batch K-Means | ClusterR | | clust.SimpleKMeans | K-Means (Weka) | RWeka | | clust.agnes | Agglomerative Nesting | cluster | | clust.ap | Affinity Propagation | apcluster | | clust.bico | BICO | stream | | clust.birch | BIRCH | stream | | clust.clara | CLARA | cluster, clue | | clust.cmeans | Fuzzy C-Means | e1071, clue | | clust.cobweb | Cobweb | RWeka | | clust.dbscan | DBSCAN | dbscan | | clust.dbscan_fpc | DBSCAN (fpc) | fpc | | clust.diana | Divisive Analysis | cluster | | clust.em | Expectation-Maximization | RWeka | | clust.fanny | Fuzzy Analysis | cluster | | clust.featureless | Featureless Clustering Learner | | | clust.ff | Farthest First | RWeka | | clust.flexmix | Finite Mixture Model | flexmix, mvtnorm | | clust.genie | Genie Hierarchical Clustering | genieclust | | clust.gmeans | G-Means | gmeans | | clust.hclust | Hierarchical Clustering | stats | | clust.hdbscan | HDBSCAN | dbscan | | clust.kcca | K-Centroids Cluster Analysis | flexclust | | clust.kkmeans | Kernel K-Means | kernlab | | clust.kmeans | K-Means | stats, clue | | clust.kmeans_rcpp | K-Means (ClusterR) | ClusterR | | clust.kmodes | K-Modes | klaR | | clust.kproto | K-Prototypes | clustMixType | | clust.mclust | Gaussian Mixture Model | mclust | | clust.meanshift | Mean Shift | LPCM | | clust.movMF | von Mises-Fisher Mixture | movMF | | clust.optics | OPTICS | dbscan | | clust.pam | Partitioning Around Medoids | cluster, clue | | clust.protoclust | Prototype Hierarchical Clustering | protoclust | | clust.skmeans | Spherical K-Means | skmeans | | clust.som | Self-Organizing Maps | kohonen | | clust.specc | Spectral Clustering | kernlab | | clust.stdbscan | ST-DBSCAN | stdbscan | | clust.tclust | Robust Trimmed Clustering | tclust | | clust.xmeans | X-Means | RWeka |

Cluster Measures

| Key | Label | Packages | |:---|:---|:---| | clust.avg_between | Average Between | cluster | | clust.avg_within | Average Within | cluster | | clust.ch | Calinski Harabasz | | | clust.davies_bouldin | Davies-Bouldin | | | clust.dunn | Dunn | cluster | | clust.dunn2 | Dunn2 | cluster | | clust.entropy | Entropy | | | clust.pearsongamma | Pearson Gamma | cluster | | clust.silhouette | Silhouette | cluster | | clust.sse_ratio | Within/Total Ratio | | | clust.wb_ratio | Within/Between Ratio | cluster | | clust.wss | Within Sum of Squares | |

Example

library(mlr3)
library(mlr3cluster)

task = tsk("usarrests")
task
#> 
#> ── <TaskClust> (50x4): US Arrests ──────────────────────────────────────────────
#> • Target:
#> • Properties: -
#> • Features (4):
#>   • int (2): Assault, UrbanPop
#>   • dbl (2): Murder, Rape

learner = lrn("clust.kmeans")
prediction = learner$train(task)$predict(task)
measures = msrs(c("clust.wss", "clust.silhouette"))
prediction$score(measures, task)
#>        clust.wss clust.silhouette 
#>     9.639903e+04     5.926554e-01

More Resources

Check out the blogpost for a more detailed introduction to the package. Also, mlr3book has a section on clustering.

Future Plans

• Add more learners and measures
• Integrate the package with mlr3pipelines (work in progress)

If you have any questions, feedback or ideas, feel free to open an issue here.

mlr3cluster documentation built on Sept. 17, 2026, 5:09 p.m.