mlr_learners_clust.tclust: Robust Trimmed Clustering Learner

In mlr3cluster: Cluster Extension for 'mlr3'

Robust Trimmed Clustering Learner

Description

Robust trimmed clustering. Each cluster is modeled by a multivariate Gaussian; the most outlying alpha fraction of observations is trimmed and labeled with cluster 0 in the returned partition. Calls tclust::tclust() from package tclust.

The k parameter is set to 2 by default since tclust::tclust() doesn't have a default value for the number of clusters. There is no predict method for tclust::tclust(), so the method returns cluster labels for the training data.

Initial parameter values

• store_x:

  • 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.tclust")
lrn("clust.tclust")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, tclust

Parameters

Id	Type	Default	Levels	Range
k	integer	-		[1, \infty)
alpha	numeric	0.05		[0, 0.5]
nstart	integer	500		[1, \infty)
niter1	integer	3		[1, \infty)
niter2	integer	20		[1, \infty)
nkeep	integer	5		[1, \infty)
iter.max	integer	-		[1, \infty)
equal.weights	logical	FALSE	TRUE, FALSE	-
restr	character	eigen	eigen, deter	-
restr.fact	numeric	12		[1, \infty)
cshape	numeric	1e+10		[1, \infty)
opt	character	HARD	HARD, MIXT	-
center	logical	FALSE	TRUE, FALSE	-
scale	logical	FALSE	TRUE, FALSE	-
store_x	logical	TRUE	TRUE, FALSE	-
parallel	logical	FALSE	TRUE, FALSE	-
n.cores	integer	-1		(-\infty, \infty)
zero_tol	numeric	1e-16		[0, \infty)
drop.empty.clust	logical	TRUE	TRUE, FALSE	-
trace	integer	0		[0, \infty)

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustTclust

Methods

Public methods

• LearnerClustTclust$new()

• LearnerClustTclust$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()

---

LearnerClustTclust$new()

Creates a new instance of this R6 class.

Usage

LearnerClustTclust$new()

---

LearnerClustTclust$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustTclust$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

García-Escudero, A L, Gordaliza, Alfonso, Matrán, Carlos, Mayo-Iscar, Agustín (2008). “A general trimming approach to robust cluster analysis.” The Annals of Statistics, 36(3), 1324–1345. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/07-AOS515")}.

Fritz, Heinrich, García-Escudero, A L, Mayo-Iscar, Agustín (2012). “tclust: An R package for a trimming approach to cluster analysis.” Journal of Statistical Software, 47(12), 1–26. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v047.i12")}.

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.pam, mlr_learners_clust.protoclust, mlr_learners_clust.skmeans, mlr_learners_clust.som, mlr_learners_clust.specc, mlr_learners_clust.stdbscan, mlr_learners_clust.xmeans

Examples

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