mlr_learners_clust.dbscan_fpc: Density-based Spatial Clustering of Applications with Noise...

mlr_learners_clust.dbscan_fpcR Documentation

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

Description

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 -

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustDBSCANfpc

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerClustDBSCANfpc$new()

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.

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

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

Examples

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

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

mlr-org/mlr3cluster documentation built on Dec. 24, 2024, 3:19 a.m.