K-Means Clustering Learner from ClusterR

K-means clustering. Calls ClusterR::KMeans_rcpp() from package ClusterR.

The clusters parameter is set to 2 by default since ClusterR::KMeans_rcpp() doesn't have a default value for the number of clusters. The predict method computes the cluster memberships for new data via the fitted centroids.

Dictionary

This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

mlr_learners$get("clust.kmeans_rcpp")
lrn("clust.kmeans_rcpp")

Meta Information

• Task type: “clust”

• Predict Types: “partition”, “prob”

• Feature Types: “logical”, “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, ClusterR

Parameters

Id	Type	Default	Levels	Range
clusters	integer	-		\([1, \infty)\)
num_init	integer	1		\([1, \infty)\)
max_iters	integer	100		\([1, \infty)\)
initializer	character	kmeans++	optimal_init, quantile_init, kmeans++, random	-
verbose	logical	FALSE	TRUE, FALSE	-
CENTROIDS	untyped	NULL		-
tol	numeric	1e-04		\([0, \infty)\)
tol_optimal_init	numeric	0.3		\([0, \infty)\)
seed	integer	1		\((-\infty, \infty)\)
threads	integer	1		\([1, \infty)\)

References

Hartigan, A J, Wong, A M (1979). “Algorithm AS 136: A K-means clustering algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108. doi:10.2307/2346830 .

Lloyd, P S (1982). “Least squares quantization in PCM.” IEEE Transactions on Information Theory, 28(2), 129–137.

Arthur, David, Vassilvitskii, Sergei (2007). “k-means++: the advantages of careful seeding.” In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package mlr3extralearners for more learners.

• Dictionary of Learners: mlr3::mlr_learners

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• Package mlr3viz for some generic visualizations.

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Other Learner: mlr_learners_clust.MBatchKMeans, mlr_learners_clust.SimpleKMeans, mlr_learners_clust.agnes, mlr_learners_clust.ap, mlr_learners_clust.bico, mlr_learners_clust.birch, mlr_learners_clust.clara, mlr_learners_clust.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, mlr_learners_clust.dbscan_fpc, mlr_learners_clust.diana, mlr_learners_clust.em, mlr_learners_clust.fanny, mlr_learners_clust.featureless, mlr_learners_clust.ff, mlr_learners_clust.flexmix, mlr_learners_clust.genie, mlr_learners_clust.gmeans, mlr_learners_clust.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kcca, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.kmodes, mlr_learners_clust.kproto, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.movMF, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.protoclust, mlr_learners_clust.skmeans, mlr_learners_clust.som, mlr_learners_clust.specc, mlr_learners_clust.stdbscan, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustKMeansRcpp

Methods

Public methods

• LearnerClustKMeansRcpp$new()

• LearnerClustKMeansRcpp$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• LearnerClust$reset()

---

LearnerClustKMeansRcpp$new()

Creates a new instance of this R6 class.

Usage

LearnerClustKMeansRcpp$new()

---

LearnerClustKMeansRcpp$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustKMeansRcpp$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.kmeans_rcpp")
print(learner)
#> 
#> ── <LearnerClustKMeansRcpp> (clust.kmeans_rcpp): K-Means (ClusterR) ────────────
#> • Model: -
#> • Parameters: clusters=2
#> • Packages: mlr3, mlr3cluster, and ClusterR
#> • Predict Types: [partition] and prob
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, exclusive, and partitional
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

# Define a Task
task = tsk("usarrests")

# Train the learner on the task
learner$train(task)

# Print the model
print(learner$model)
#> KMeans Cluster
#>  Call: ClusterR::KMeans_rcpp(data = task$data(), clusters = 2L) 
#>  Data cols: 4 
#>  Centroids: 2 
#>  BSS/SS: 0.72907 
#>  SS: 355807.8 = 96399.03 (WSS) + 259408.8 (BSS)

# Make predictions for the task
prediction = learner$predict(task)

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.1033191