mlr3cluster

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:

devtools::install_github("mlr-org/mlr3cluster")

Feature Overview

The current version of mlr3cluster contains:

A selection of 17 clustering learners that represent a wide variety of clusterers: partitional, hierarchical, fuzzy, etc.
A selection of 4 performance measures
One built-in task 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

ID	Learner	Package
clust.agnes	Agglomerative Hierarchical Clustering	cluster
clust.ap	Affinity Propagation Clustering	apcluster
clust.cmeans	Fuzzy C-Means Clustering	e1071
clust.cobweb	Cobweb Clustering Algorithm	RWeka
clust.dbscan	Density-based Clustering	dbscan
clust.diana	Divisive Hierarchical Clustering	cluster
clust.em	Expectation-Maximization Clustering	RWeka
clust.fanny	Fuzzy Clustering	cluster
clust.featureless	Simple Featureless Clustering	mlr3cluster
clust.ff	FarthestFirst Clustering Algorithm	RWeka
clust.hclust	Agglomerative Hierarchical Clustering	stats
clust.kkmeans	Kernel K-Means Clustering	kernlab
clust.kmeans	K-Means Clustering	stats
clust.MBatchKMeans	Mini Batch K-Means Clustering	ClusterR
clust.meanshift	Mean Shift Clustering	LPCM
clust.pam	Clustering Around Medoids	cluster
clust.SimpleKMeans	K-Means Clustering (WEKA)	RWeka
clust.xmeans	K-Means with Automatic Determination of k	RWeka

Cluster Measures

ID	Measure	Package
clust.db	Davies-Bouldin Cluster Separation	clusterCrit
clust.dunn	Dunn index	clusterCrit
clust.ch	Calinski Harabasz Pseudo F-Statistic	clusterCrit
clust.silhouette	Rousseeuw’s Silhouette Quality Index	clusterCrit
clust.wss	Within Sum of Squares	clusterCrit

Example

library(mlr3)
library(mlr3cluster)

task = mlr_tasks$get("usarrests")
learner = mlr_learners$get("clust.kmeans")
learner$train(task)
preds = learner$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.