A LearnerClust for k-means clustering implemented in stats::kmeans()
.
stats::kmeans()
doesn't have a default value for the number of clusters.
Therefore, the centers
parameter here is set to 2 by default.
The predict method uses clue::cl_predict()
to compute the
cluster memberships for new data.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, 'stats', clue
Parameters
Id | Type | Default | Levels | Range |
centers | untyped | 2 | - | |
iter.max | integer | 10 | \([1, \infty)\) | |
algorithm | character | Hartigan-Wong | Hartigan-Wong, Lloyd, Forgy, MacQueen | - |
nstart | integer | 1 | \([1, \infty)\) | |
trace | integer | 0 | \([0, \infty)\) |
References
Forgy, W E (1965). “Cluster analysis of multivariate data: efficiency versus interpretability of classifications.” Biometrics, 21, 768--769.
Hartigan, A J, Wong, A M (1979). “Algorithm AS 136: A K-means clustering algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100--108. doi:10.2307/2346830 .
Lloyd, P S (1982). “Least squares quantization in PCM.” IEEE Transactions on Information Theory, 28(2), 129--137.
MacQueen, James (1967). “Some methods for classification and analysis of multivariate observations.” In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, 281--297.
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.
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.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.mclust
,
mlr_learners_clust.meanshift
,
mlr_learners_clust.optics
,
mlr_learners_clust.pam
,
mlr_learners_clust.xmeans
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustKMeans
Examples
if (requireNamespace("stats") && requireNamespace("clue")) {
learner = mlr3::lrn("clust.kmeans")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustKMeans:clust.kmeans>: K-Means
#> * Model: -
#> * Parameters: centers=2
#> * Packages: mlr3, mlr3cluster, stats, clue
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "centers" "iter.max" "algorithm" "nstart" "trace"