mlr3cluster (version 0.1.9)

mlr_learners_clust.SimpleKMeans: K-Means Clustering Learner from Weka

Description

A LearnerClust for Simple K Means clustering implemented in RWeka::SimpleKMeans(). The predict method uses RWeka::predict.Weka_clusterer() to compute the cluster memberships for new data.

Dictionary

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

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

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, RWeka

Parameters

Id	Type	Default	Levels	Range
A	untyped	weka.core.EuclideanDistance		-
C	logical	FALSE	TRUE, FALSE	-
fast	logical	FALSE	TRUE, FALSE	-
I	integer	100		\([1, \infty)\)
init	integer	0		\([0, 3]\)
M	logical	FALSE	TRUE, FALSE	-
max_candidates	integer	100		\([1, \infty)\)
min_density	integer	2		\([1, \infty)\)
N	integer	2		\([1, \infty)\)
num_slots	integer	1		\([1, \infty)\)
O	logical	FALSE	TRUE, FALSE	-
periodic_pruning	integer	10000		\([1, \infty)\)
S	integer	10		\([0, \infty)\)
t2	numeric	-1		\((-\infty, \infty)\)
t1	numeric	-1.5		\((-\infty, \infty)\)
V	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustSimpleKMeans

Methods

Public methods

• LearnerClustSimpleKMeans$new()

• LearnerClustSimpleKMeans$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$train()
• mlr3cluster::LearnerClust$reset()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustSimpleKMeans$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustSimpleKMeans$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76--77.

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

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

• 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: 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.

• 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.agnes, mlr_learners_clust.ap, 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.kmeans, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Examples

if (requireNamespace("RWeka")) {
  learner = mlr3::lrn("clust.SimpleKMeans")
  print(learner)

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