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mlr3cluster

Package website: release | dev

Cluster analysis for mlr3.

r-cmd-check CRAN status StackOverflow Mattermost

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.