Agglomerative Hierarchical Clustering Learner

A LearnerClust for agglomerative hierarchical clustering implemented in cluster::agnes(). The predict method uses stats::cutree() which cuts the tree resulting from hierarchical clustering into specified number of groups (see parameter k). The default number for k is 2.

Dictionary

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

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

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustAgnes

Methods

Public methods

• LearnerClustAgnes$new()

• LearnerClustAgnes$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustAgnes$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustAgnes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("clust.agnes")
print(learner)
#> <LearnerClustAgnes:clust.agnes>
#> * Model: -
#> * Parameters: k=2
#> * Packages: cluster
#> * Predict Type: partition
#> * Feature types: logical, integer, numeric
#> * Properties: complete, exclusive, hierarchical

# available parameters:
learner$param_set$ids()
#> [1] "metric"     "stand"      "method"     "trace.lev"  "k"         
#> [6] "par.method"