The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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 Nesting	cluster
clust.ap	Affinity Propagation	apcluster
clust.bico	BICO	stream
clust.birch	BIRCH	stream
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.hclust	Hierarchical Clustering	stats
clust.hdbscan	HDBSCAN	dbscan
clust.kkmeans	Kernel K-Means	kernlab
clust.kmeans	K-Means	stats, clue
clust.mclust	Gaussian Mixture Model	mclust
clust.meanshift	Mean Shift	LPCM
clust.optics	OPTICS	dbscan
clust.pam	Partitioning Around Medoids	cluster, clue
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")
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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.