ST-DBSCAN Clustering Learner

ST-DBSCAN (spatio-temporal density-based spatial clustering of applications with noise) clustering. Calls stdbscan::st_dbscan() from package stdbscan.

The task must have exactly 3 features: the first two features (in the task's feature order, which is alphabetical for newly created tasks) are used as the spatial coordinates and the third feature as the temporal coordinate.

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.stdbscan")
lrn("clust.stdbscan")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, stdbscan

Parameters

Id	Type	Default	Levels	Range
eps_spatial	numeric	-		\([0, \infty)\)
eps_temporal	numeric	-		\([0, \infty)\)
min_pts	integer	-		\([1, \infty)\)
borderPoints	logical	TRUE	TRUE, FALSE	-
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

Birant, Derya, Kut, Alp (2007). “ST-DBSCAN: An algorithm for clustering spatial-temporal data.” Data & Knowledge Engineering, 60(1), 208–221. doi:10.1016/j.datak.2006.01.013 .

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: mlr3::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.

• Package mlr3viz for some generic visualizations.

• 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.bico, mlr_learners_clust.birch, mlr_learners_clust.clara, mlr_learners_clust.cmeans, mlr_learners_clust.cobweb, mlr_learners_clust.dbscan, 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.flexmix, mlr_learners_clust.genie, mlr_learners_clust.hclust, mlr_learners_clust.hdbscan, mlr_learners_clust.kcca, mlr_learners_clust.kkmeans, mlr_learners_clust.kmeans, mlr_learners_clust.kproto, mlr_learners_clust.mclust, mlr_learners_clust.meanshift, mlr_learners_clust.movMF, mlr_learners_clust.optics, mlr_learners_clust.pam, mlr_learners_clust.protoclust, mlr_learners_clust.skmeans, mlr_learners_clust.som, mlr_learners_clust.specc, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustSTDBSCAN

Methods

Public methods

• LearnerClustSTDBSCAN$new()

• LearnerClustSTDBSCAN$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• LearnerClust$reset()

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LearnerClustSTDBSCAN$new()

Creates a new instance of this R6 class.

Usage

LearnerClustSTDBSCAN$new()

---

LearnerClustSTDBSCAN$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustSTDBSCAN$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.stdbscan")
print(learner)
#> 
#> ── <LearnerClustSTDBSCAN> (clust.stdbscan): ST-DBSCAN ──────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cluster, and stdbscan
#> • Predict Types: [partition]
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: density, exclusive, and partial
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'