BIRCH Clustering Learner

BIRCH (balanced iterative reducing clustering using hierarchies) clustering. Calls stream::DSC_BIRCH() from package stream.

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.birch")
lrn("clust.birch")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, stream

Parameters

Id	Type	Default	Range
threshold	numeric	-	\([0, \infty)\)
branching	integer	-	\([1, \infty)\)
maxLeaf	integer	-	\([1, \infty)\)
maxMem	integer	0	\([0, \infty)\)
outlierThreshold	numeric	0.25	\((-\infty, \infty)\)

References

Zhang, Tian, Ramakrishnan, Raghu, Livny, Miron (1996). “BIRCH: An Efficient Data Clustering Method for Very Large Databases.” ACM sigmod record, 25(2), 103–114.

Zhang, Tian, Ramakrishnan, Raghu, Livny, Miron (1997). “BIRCH: A new data clustering algorithm and its applications.” Data Mining and Knowledge Discovery, 1, 141–182.

Hahsler M, Bolaños M, Forrest J (2017). “Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R.” Journal of Statistical Software, 76(14), 1–50. doi:10.18637/jss.v076.i14 .

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.

• 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.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.stdbscan, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustBIRCH

Methods

Public methods

• LearnerClustBIRCH$new()

• LearnerClustBIRCH$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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LearnerClustBIRCH$new()

Creates a new instance of this R6 class.

Usage

LearnerClustBIRCH$new()

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LearnerClustBIRCH$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustBIRCH$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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