mlr_learners_clust.dbscan {mlr3cluster}R Documentation

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

Description

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

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

Inherited methods

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.

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

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

Examples

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

  # available parameters:
  learner$param_set$ids()
}

[Package mlr3cluster version 0.1.9 Index]