Self-Organizing Maps Clustering Learner

Self-organizing map (Kohonen network) clustering. Calls kohonen::som() from package kohonen.

Each map unit corresponds to a cluster, so the number of clusters is xdim * ydim. Grid dimensions, topology, and neighbourhood function are exposed directly as parameters and forwarded to kohonen::somgrid(). The predict method uses kohonen::predict.kohonen() to assign new data to the closest unit.

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.som")
lrn("clust.som")

Meta Information

• Task type: “clust”

• Predict Types: “partition”

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

• Required Packages: mlr3, mlr3cluster, kohonen

Parameters

Id	Type	Default	Levels	Range
xdim	integer	8		\([1, \infty)\)
ydim	integer	6		\([1, \infty)\)
topo	character	rectangular	rectangular, hexagonal	-
neighbourhood.fct	character	bubble	bubble, gaussian	-
toroidal	logical	FALSE	TRUE, FALSE	-
rlen	integer	100		\([1, \infty)\)
alpha	untyped	c(0.05, 0.01)		-
radius	untyped	-		-
user.weights	untyped	1		-
maxNA.fraction	numeric	0		\([0, 1]\)
keep.data	logical	TRUE	TRUE, FALSE	-
dist.fcts	untyped	NULL		-
mode	character	online	online, batch, pbatch	-
cores	integer	-1		\((-\infty, \infty)\)
init	untyped	-		-
normalizeDataLayers	logical	TRUE	TRUE, FALSE	-

References

Kohonen, Teuvo (1990). “The self-organizing map.” Proceedings of the IEEE, 78(9), 1464–1480. doi:10.1109/5.58325 .

Wehrens, Ron, Kruisselbrink, Johannes (2018). “Flexible self-organizing maps in kohonen 3.0.” Journal of Statistical Software, 87(7), 1–18. doi:10.18637/jss.v087.i07 .

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.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.specc, mlr_learners_clust.stdbscan, mlr_learners_clust.tclust, mlr_learners_clust.xmeans

Super classes

mlr3::Learner -> LearnerClust -> LearnerClustSOM

Methods

Public methods

• LearnerClustSOM$new()

• LearnerClustSOM$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()

---

LearnerClustSOM$new()

Creates a new instance of this R6 class.

Usage

LearnerClustSOM$new()

---

LearnerClustSOM$clone()

The objects of this class are cloneable with this method.

Usage

LearnerClustSOM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("clust.som")
print(learner)
#> 
#> ── <LearnerClustSOM> (clust.som): Self-Organizing Maps ─────────────────────────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3cluster, and kohonen
#> • Predict Types: [partition]
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: complete, exclusive, and partitional
#> • Other settings: use_weights = 'error', predict_raw = 'FALSE'

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

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

# Print the model
print(learner$model)
#> SOM of size 8x6 with a rectangular topology.
#> Training data included.

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

# Score the predictions
prediction$score(task = task)
#> clust.dunn 
#>  0.1909076