mlr_learners_clust.pam: Partitioning Around Medoids Clustering Learner

In mlr3cluster: Cluster Extension for 'mlr3'

Partitioning Around Medoids Clustering Learner

Description

Partitioning Around Medoids (PAM) clustering. Calls cluster::pam() from package cluster.

The k parameter is set to 2 by default since cluster::pam() doesn't have a default value for the number of clusters. The predict method uses clue::cl_predict() to compute the cluster memberships for new data.

Initial parameter values

• keep.diss:

  • Actual default: n < 100, where n is the number of observations.

  • Adjusted default: FALSE.

  • Reason for change: Avoid storing the dissimilarity matrix in the model to save memory.

• keep.data:

  • Actual default: TRUE.

  • Adjusted default: FALSE.

  • Reason for change: Avoid storing the training data in the model to save memory.

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.pam")
lrn("clust.pam")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, cluster, clue

Parameters

Id	Type	Default	Levels	Range
k	integer	-		[1, \infty)
metric	character	euclidean	euclidean, manhattan	-
medoids	untyped	NULL		-
nstart	integer	1		[1, \infty)
stand	logical	FALSE	TRUE, FALSE	-
do.swap	logical	TRUE	TRUE, FALSE	-
keep.diss	logical	-	TRUE, FALSE	-
keep.data	logical	TRUE	TRUE, FALSE	-
pamonce	untyped	FALSE		-
variant	character	original	original, o_1, o_2, f_3, f_4, f_5, faster	-
trace.lev	integer	0		[0, \infty)

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustPAM

Methods

Public methods

• LearnerClustPAM$new()

• LearnerClustPAM$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()
• LearnerClust$reset()

---

LearnerClustPAM$new()

Creates a new instance of this R6 class.

Usage

LearnerClustPAM$new()

---

LearnerClustPAM$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustPAM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Reynolds, P A, Richards, Graeme, de la Iglesia, Beatriz, Rayward-Smith, J V (2006). “Clustering rules: a comparison of partitioning and hierarchical clustering algorithms.” Journal of Mathematical Modelling and Algorithms, 5, 475–504.

Schubert, Erich, Rousseeuw, J P (2019). “Faster k-medoids clustering: improving the PAM, CLARA, and CLARANS algorithms.” In Similarity Search and Applications: 12th International Conference, SISAP 2019, Newark, NJ, USA, October 2–4, 2019, Proceedings 12, 171–187. Springer.

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.

• Package mlr3viz for some generic visualizations.

• 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.clara, 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.flexmix, mlr_learners_clust.genie, mlr_learners_clust.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kcca, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.kproto, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.movMF, mlr_learners_clust.optics, mlr_learners_clust.protoclust, mlr_learners_clust.skmeans, mlr_learners_clust.som, mlr_learners_clust.specc, mlr_learners_clust.stdbscan, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Examples

# Define the Learner and set parameter values
learner = lrn("clust.pam")
print(learner)

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

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

# Print the model
print(learner$model)

# Make predictions for the task
prediction = learner$predict(task)

# Score the predictions
prediction$score(task = task)

mlr3cluster documentation built on June 11, 2026, 5:06 p.m.