mlr_learners_clust.bico | R Documentation |
BICO (Fast computation of k-means coresets in a data stream) clustering.
Calls stream::DSC_BICO()
from stream.
This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn()
:
mlr_learners$get("clust.bico") lrn("clust.bico")
Task type: “clust”
Predict Types: “partition”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, stream
Id | Type | Default | Range |
k | integer | 5 | [1, \infty) |
space | integer | 10 | [1, \infty) |
p | integer | 10 | [1, \infty) |
iterations | integer | 10 | [1, \infty) |
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustBICO
new()
Creates a new instance of this R6 class.
LearnerClustBICO$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustBICO$clone(deep = FALSE)
deep
Whether to make a deep clone.
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v076.i14")}.
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
if (requireNamespace("stream")) {
learner = mlr3::lrn("clust.bico")
print(learner)
# available parameters:
learner$param_set$ids()
}
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