BICO Clustering Learner

BICO (Fast computation of k-means coresets in a data stream) clustering. Calls stream::DSC_BICO() from 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.bico")
lrn("clust.bico")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, mlr3cluster, stream

Parameters

Id	Type	Default	Range
k	integer	5	\([1, \infty)\)
space	integer	10	\([1, \infty)\)
p	integer	10	\([1, \infty)\)
iterations	integer	10	\([1, \infty)\)

References

Fichtenberger, Hendrik, Gille, Marc, Schmidt, Melanie, Schwiegelshohn, Chris, Sohler, Christian (2013). “BICO: BIRCH Meets Coresets for k-Means Clustering.” In Algorithms–ESA 2013: 21st Annual European Symposium, Sophia Antipolis, France, September 2-4, 2013. Proceedings 21, 481–492. Springer.

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.birch, 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.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

Super classes

mlr3::Learner -> mlr3cluster::LearnerClust -> LearnerClustBICO

Methods

Public methods

• LearnerClustBICO$new()

• LearnerClustBICO$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()
• mlr3cluster::LearnerClust$reset()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustBICO$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustBICO$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

# Define a Task
task = tsk("usarrests")

# Train the learner on the task
learner$train(task)

# Print the model
print(learner$model)
#> BICO - Fast computation of k-means coresets 
#> Class: DSC_BICO, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 10 
#> Number of macro-clusters: 5 

# Make predictions for the task
prediction = learner$predict(task)

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>   0.137278