In mlr-org/mlr3cluster: Cluster Extension for 'mlr3'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

library(data.table)
library(mlr3cluster)
library(mlr3misc)
lrn_clust = as.data.table(mlr3::mlr_learners)[task_type == "clust", .(key, label, packages)]
msr_clust = as.data.table(mlr3::mlr_measures)[task_type == "clust", .(key, label, packages)]

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 r nrow(lrn_clust) clustering learners that represent a wide variety of clusterers: partitional, hierarchical, fuzzy, etc.
A selection of r nrow(msr_clust) 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

cran_pkg = function(pkgs) {
  pkgs = fifelse(
    pkgs %chin% c("stats", "graphics", "datasets"),
    pkgs, sprintf("[%1$s](https://cran.r-project.org/package=%1$s)", pkgs)
  )
  toString(pkgs)
}

lrn_clust[, packages := map(packages, function(x) setdiff(x, c("mlr3", "mlr3cluster")))]
lrn_clust[, `:=`(
  key = sprintf("[%1$s](https://mlr3cluster.mlr-org.com/reference/mlr_learners_%1$s)", key),
  packages = map_chr(packages, cran_pkg)
)]
knitr::kable(lrn_clust, format = "markdown", col.names = tools::toTitleCase(names(lrn_clust)))

Cluster Measures

msr_clust[, packages := map(packages, function(x) setdiff(x, c("mlr3", "mlr3cluster")))]
msr_clust[, `:=`(
  key = sprintf("[%1$s](https://mlr3cluster.mlr-org.com/reference/mlr_measures_%1$s)", key),
  packages = map_chr(packages, cran_pkg)
)]
knitr::kable(msr_clust, format = "markdown", col.names = tools::toTitleCase(names(msr_clust)))

Example

library(mlr3)
library(mlr3cluster)

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