Density-based Spatial Clustering of Applications with Noise (DBSCAN) Clustering Learner

DBSCAN (Density-based spatial clustering of applications with noise) clustering. Calls fpc::dbscan() from fpc.

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.dbscan_fpc")
lrn("clust.dbscan_fpc")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, fpc

Parameters

Id	Type	Default	Levels	Range
eps	numeric	-		\([0, \infty)\)
MinPts	integer	5		\([0, \infty)\)
scale	logical	FALSE	TRUE, FALSE	-
method	character	-	hybrid, raw, dist	-
seeds	logical	TRUE	TRUE, FALSE	-
showplot	untyped	FALSE		-
countmode	untyped	NULL		-

References

Ester, Martin, Kriegel, Hans-Peter, Sander, Jörg, Xu, Xiaowei, others (1996). “A density-based algorithm for discovering clusters in large spatial databases with noise.” In kdd, volume 96 number 34, 226–231.

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.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 -> LearnerClustDBSCANfpc

Methods

Public methods

• LearnerClustDBSCANfpc$new()

• LearnerClustDBSCANfpc$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• 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

LearnerClustDBSCANfpc$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustDBSCANfpc$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustDBSCANfpc:clust.dbscan_fpc>: Density-Based Clustering with fpc
#> * Model: -
#> * Parameters: MinPts=5, scale=FALSE, seeds=TRUE, showplot=FALSE
#> * Packages: mlr3, mlr3cluster, fpc
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, density, exclusive
#> [1] "eps"       "MinPts"    "scale"     "method"    "seeds"     "showplot" 
#> [7] "countmode"