Density-Based Clustering Learner

A LearnerClust for density-based clustering implemented in dbscan::dbscan(). The predict method uses dbscan::predict.dbscan_fast() to compute the cluster memberships for new data.

Dictionary

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

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

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, dbscan

Parameters

Id	Type	Default	Levels	Range
eps	numeric	-		\([0, \infty)\)
minPts	integer	5		\([0, \infty)\)
borderPoints	logical	TRUE	TRUE, FALSE	-
weights	untyped	-		-
search	character	kdtree	kdtree, linear, dist	-
bucketSize	integer	10		\([1, \infty)\)
splitRule	character	SUGGEST	STD, MIDPT, FAIR, SL_MIDPT, SL_FAIR, SUGGEST	-
approx	numeric	0		\((-\infty, \infty)\)

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustDBSCAN

Methods

Public methods

• LearnerClustDBSCAN$new()

• LearnerClustDBSCAN$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustDBSCAN$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustDBSCAN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("dbscan")) {
  learner = mlr3::lrn("clust.dbscan")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustDBSCAN:clust.dbscan>: Density-Based Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, dbscan
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "eps"          "minPts"       "borderPoints" "weights"      "search"      
#> [6] "bucketSize"   "splitRule"    "approx"