Affinity Propagation Clustering Learner

A LearnerClust for Affinity Propagation clustering implemented in apcluster::apcluster(). apcluster::apcluster() doesn't have set a default for similarity function. The predict method computes the closest cluster exemplar to find the cluster memberships for new data. The code is taken from StackOverflow answer by the apcluster package maintainer.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("clust.ap")
lrn("clust.ap")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, apcluster

Parameters

Id	Type	Default	Levels	Range
s	untyped	-		-
p	untyped	NA		-
q	numeric	-		\([0, 1]\)
maxits	integer	1000		\([1, \infty)\)
convits	integer	100		\([1, \infty)\)
lam	numeric	0.9		\([0.5, 1]\)
includeSim	logical	FALSE	TRUE, FALSE	-
details	logical	FALSE	TRUE, FALSE	-
nonoise	logical	FALSE	TRUE, FALSE	-
seed	integer	-		\((-\infty, \infty)\)

References

Bodenhofer, Ulrich, Kothmeier, Andreas, Hochreiter, Sepp (2011). “APCluster: an R package for affinity propagation clustering.” Bioinformatics, 27(17), 2463--2464.

Frey, J B, Dueck, Delbert (2007). “Clustering by passing messages between data points.” science, 315(5814), 972--976.

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: 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.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 -> LearnerClustAP

Methods

Public methods

• LearnerClustAP$new()

• LearnerClustAP$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$train()
• mlr3cluster::LearnerClust$reset()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClustAP$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustAP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("apcluster")) {
  learner = mlr3::lrn("clust.ap")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerClustAP:clust.ap>: Affinity Propagation Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, apcluster
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#>  [1] "s"          "p"          "q"          "maxits"     "convits"   
#>  [6] "lam"        "includeSim" "details"    "nonoise"    "seed"