mlr_learners_clust.kmeans: K-Means Clustering Learner

mlr_learners_clust.kmeansR Documentation

K-Means Clustering Learner

Description

A LearnerClust for k-means clustering implemented in stats::kmeans(). stats::kmeans() doesn't have a default value for the number of clusters. Therefore, the centers parameter here is set to 2 by default. The predict method uses clue::cl_predict() to compute the cluster memberships for new data.

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")
lrn("clust.kmeans")

Meta Information

  • Task type: “clust”

  • Predict Types: “partition”

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

  • Required Packages: mlr3, mlr3cluster, 'stats', clue

Parameters

Id Type Default Levels Range
centers untyped - -
iter.max integer 10 [1, \infty)
algorithm character Hartigan-Wong Hartigan-Wong, Lloyd, Forgy, MacQueen -
nstart integer 1 [1, \infty)
trace integer 0 [0, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustKMeans

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustKMeans$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustKMeans$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Forgy, W E (1965). “Cluster analysis of multivariate data: efficiency versus interpretability of classifications.” Biometrics, 21, 768–769.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/2346830")}.

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

MacQueen, James (1967). “Some methods for classification and analysis of multivariate observations.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, 281–297.

See Also

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.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.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kkmeans, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("stats") && requireNamespace("clue")) {
  learner = mlr3::lrn("clust.kmeans")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}

mlr-org/mlr3cluster documentation built on Dec. 24, 2024, 3:19 a.m.