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 22 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.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.