mlr_learners_clust.hclust | R Documentation |
A LearnerClust for agglomerative hierarchical clustering implemented in stats::hclust()
.
Difference Calculation is done by stats::dist()
This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn()
:
mlr_learners$get("clust.hclust") lrn("clust.hclust")
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, 'stats'
Id | Type | Default | Levels | Range |
method | character | complete | ward.D, ward.D2, single, complete, average, mcquitty, median, centroid | - |
members | untyped | NULL | - | |
distmethod | character | euclidean | euclidean, maximum, manhattan, canberra, binary, minkowski | - |
diag | logical | FALSE | TRUE, FALSE | - |
upper | logical | FALSE | TRUE, FALSE | - |
p | numeric | 2 | (-\infty, \infty) |
|
k | integer | 2 | [1, \infty) |
|
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustHclust
new()
Creates a new instance of this R6 class.
LearnerClustHclust$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustHclust$clone(deep = FALSE)
deep
Whether to make a deep clone.
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")}.
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.
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.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
if (requireNamespace("stats")) {
learner = mlr3::lrn("clust.hclust")
print(learner)
# available parameters:
learner$param_set$ids()
}
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