Mean Shift Clustering Learner

A LearnerClust for Mean Shift clustering implemented in LPCM::ms(). There is no predict method for LPCM::ms(), so the method returns cluster labels for the 'training' data.

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.meanshift")
lrn("clust.meanshift")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, LPCM

Parameters

Id	Type	Default	Range
h	untyped	-	-
subset	untyped	-	-
scaled	integer	1	\([0, \infty)\)
iter	integer	200	\([1, \infty)\)
thr	numeric	0.01	\((-\infty, \infty)\)

References

Cheng, Yizong (1995). “Mean shift, mode seeking, and clustering.” IEEE transactions on pattern analysis and machine intelligence, 17(8), 790–799.

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.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.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.mclust, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustMeanShift

Methods

Public methods

• LearnerClustMeanShift$new()

• LearnerClustMeanShift$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

LearnerClustMeanShift$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustMeanShift$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.meanshift")
print(learner)
#> 
#> ── <LearnerClustMeanShift> (clust.meanshift): Mean Shift Clustering ────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cluster, and LPCM
#> • Predict Types: [partition]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, exclusive, and partitional
#> • 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)
#> 
#> Type plot( $ learner model ) to see a graphical display of the fitted object. 
#> 
#> Type names( $ learner model ) to see an overview of items available. 
#> 
#> The data have been scaled by dividing through 
#> 292 16.6 38.7 59

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.6147292