Skip to contents

OPTICS (Ordering points to identify the clustering structure) point ordering clustering. Calls dbscan::optics() from dbscan.

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.optics")
lrn("clust.optics")

Meta Information

  • Task type: “clust”

  • Predict Types: “partition”

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

  • Required Packages: mlr3, mlr3cluster, dbscan

Parameters

IdTypeDefaultLevelsRange
epsnumericNULL\([0, \infty)\)
minPtsinteger5\([0, \infty)\)
searchcharacterkdtreekdtree, linear, dist-
bucketSizeinteger10\([1, \infty)\)
splitRulecharacterSUGGESTSTD, MIDPT, FAIR, SL_MIDPT, SL_FAIR, SUGGEST-
approxnumeric0\((-\infty, \infty)\)
eps_clnumeric-\([0, \infty)\)

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 .

Ankerst, Mihael, Breunig, M M, Kriegel, Hans-Peter, Sander, Jörg (1999). “OPTICS: Ordering points to identify the clustering structure.” ACM Sigmod record, 28(2), 49–60.

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustOPTICS

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

LearnerClustOPTICS$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustOPTICS:clust.optics>: OPTICS Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, dbscan
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, density, exclusive
#> [1] "eps"        "minPts"     "search"     "bucketSize" "splitRule" 
#> [6] "approx"     "eps_cl"