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 mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn():

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

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, cluster

Parameters

Id	Type	Default	Levels	Range
metric	character	euclidean	euclidean, manhattan	-
stand	logical	FALSE	TRUE, FALSE	-
method	character	average	average, single, complete, ward, weighted, flexible, gaverage	-
trace.lev	integer	0		\([0, \infty)\)
k	integer	2		\([1, \infty)\)
par.method	untyped	-		-

References

Kaufman, Leonard, Rousseeuw, J P (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.

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: 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.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.kmeans, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Super classes

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

Methods

Public methods

• LearnerClustAgnes$new()

• LearnerClustAgnes$clone()

Inherited methods

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

---

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

# Define the Learner and set parameter values
learner = lrn("clust.agnes")
print(learner)
#> 
#> ── <LearnerClustAgnes> (clust.agnes): Agglomerative Hierarchical Clustering ────
#> • Model: -
#> • Parameters: k=2
#> • Packages: mlr3, mlr3cluster, and cluster
#> • Predict Types: [partition]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, exclusive, and hierarchical
#> • Other settings: use_weights = 'error'

# Define a Task
task = tsk("usarrests")

# Train the learner on the task
learner$train(task)

# Print the model
print(learner$model)
#> Call:	 cluster::agnes(x = task$data(), diss = FALSE) 
#> Agglomerative coefficient:  0.9073773 
#> Order of objects:
#>  [1]  1 18  8 13 32 22 28  2 24 40  3 31 20  5  9 33  4 42 25  6 43 10 21 30 39
#> [26] 36 46 50 37 47  7 38 14 16 35 44 12 17 26 27 11 15 29 49 23 34 45 19 41 48
#> Height (summary):
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   2.291  12.439  16.425  24.847  28.012 152.314 
#> 
#> Available components:
#> [1] "order"  "height" "ac"     "merge"  "diss"   "call"   "method" "data"  

# Make predictions for the task
prediction = learner$predict(task)
#> Warning: Learner 'clust.agnes' doesn't predict on new data and predictions may not make sense on new data.

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.1532626