mlr_learners_clust.hclust: Agglomerative Hierarchical Clustering Learner

mlr_learners_clust.hclustR Documentation

Agglomerative Hierarchical Clustering Learner

Description

A LearnerClust for agglomerative hierarchical clustering implemented in stats::hclust(). Difference Calculation is done by stats::dist()

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.hclust")
lrn("clust.hclust")

Meta Information

  • Task type: “clust”

  • Predict Types: “partition”

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

  • Required Packages: mlr3, mlr3cluster, 'stats'

Parameters

Id Type Default Levels Range
method character complete ward.D, ward.D2, single, complete, average, mcquitty, median, centroid -
members untyped NULL -
distmethod character euclidean euclidean, maximum, manhattan, canberra, binary, minkowski -
diag logical FALSE TRUE, FALSE -
upper logical FALSE TRUE, FALSE -
p numeric 2 (-\infty, \infty)
k integer 2 [1, \infty)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustHclust

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustHclust$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerClustHclust$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

Becker, A R, Chambers, M J, Wilks, R A (1988). The New S Language. Wadsworth & Brooks/Cole.

Everitt, S B (1974). Cluster Analysis. Heinemann Educational Books.

Hartigan, A J (1975). Clustering Algorithms. John Wiley & Sons.

Sneath, HA P, Sokal, R R (1973). Numerical Taxonomy. Freeman.

Anderberg, R M (1973). Cluster Analysis for Applications. Academic Press.

Gordon, David A (1999). Classification, 2 edition. Chapman and Hall / CRC.

Murtagh, Fionn (1985). “Multidimensional Clustering Algorithms.” In COMPSTAT Lectures 4. Physica-Verlag.

McQuitty, L L (1966). “Similarity Analysis by Reciprocal Pairs for Discrete and Continuous Data.” Educational and Psychological Measurement, 26(4), 825–831. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/001316446602600402")}.

Legendre, Pierre, Legendre, Louis (2012). Numerical Ecology, 3 edition. Elsevier Science BV.

Murtagh, Fionn, Legendre, Pierre (2014). “Ward's Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward's Criterion?” Journal of Classification, 31, 274–295. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s00357-014-9161-z")}.

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.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("stats")) {
  learner = mlr3::lrn("clust.hclust")
  print(learner)

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

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