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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

IdTypeDefaultLevelsRange
epsnumeric-\([0, \infty)\)
MinPtsinteger5\([0, \infty)\)
scalelogicalFALSETRUE, FALSE-
methodcharacter-hybrid, raw, dist-
seedslogicalTRUETRUE, FALSE-
showplotuntypedFALSE-
countmodeuntypedNULL-

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.

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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"