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 24 clustering learners that represent a wide variety of clusterers: partitional, hierarchical, fuzzy, etc.
• A selection of 4 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 Hierarchical Clustering | cluster | | clust.ap | Affinity Propagation Clustering | apcluster | | clust.bico | BICO Clustering | stream | | clust.birch | BIRCH Clustering | stream | | clust.cmeans | Fuzzy C-Means Clustering Learner | e1071 | | clust.cobweb | Cobweb Clustering | RWeka | | clust.dbscan | Density-Based Clustering | dbscan | | clust.dbscan_fpc | Density-Based Clustering with fpc | fpc | | clust.diana | Divisive Hierarchical Clustering | cluster | | clust.em | Expectation-Maximization Clustering | RWeka | | clust.fanny | Fuzzy Analysis Clustering | cluster | | clust.featureless | Featureless Clustering | | | clust.ff | Farthest First Clustering | RWeka | | clust.hclust | Agglomerative Hierarchical Clustering | stats | | clust.hdbscan | HDBSCAN Clustering | dbscan | | clust.kkmeans | Kernel K-Means | kernlab | | clust.kmeans | K-Means | stats, clue | | clust.mclust | Gaussian Mixture Models Clustering | mclust | | clust.meanshift | Mean Shift Clustering | LPCM | | clust.optics | OPTICS Clustering | dbscan | | clust.pam | Partitioning Around Medoids | cluster | | clust.xmeans | X-means | RWeka |

Cluster Measures

| Key | Label | Packages | |:---|:---|:---| | clust.ch | Calinski Harabasz | fpc | | clust.dunn | Dunn | fpc | | clust.silhouette | Silhouette | cluster | | clust.wss | Within Sum of Squares | fpc |

Example

library(mlr3)
library(mlr3cluster)

task = tsk("usarrests")
learner = lrn("clust.kmeans")
learner$train(task)
prediction = learner$predict(task = task)

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 April 4, 2025, 2 a.m.