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

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

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		\((-\infty, \infty)\)

References

Hahsler M, Piekenbrock M, Doran D (2019). “dbscan: Fast Density-Based Clustering with R.” Journal of Statistical Software, 91(1), 1--30. doi:10.18637/jss.v091.i01 .

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: 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.cmeans, mlr_learners_clust.cobweb, 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.meanshift, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.xmeans

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()

---

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"