K-Means Clustering Learner from Weka
Source:R/LearnerClustSimpleKMeans.R
mlr_learners_clust.SimpleKMeans.Rd
A LearnerClust for Simple K Means clustering implemented in RWeka::SimpleKMeans()
.
The predict method uses RWeka::predict.Weka_clusterer()
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, RWeka
Parameters
Id | Type | Default | Levels | Range |
A | untyped | weka.core.EuclideanDistance | - | |
C | logical | FALSE | TRUE, FALSE | - |
fast | logical | FALSE | TRUE, FALSE | - |
I | integer | 100 | \([1, \infty)\) | |
init | integer | 0 | \([0, 3]\) | |
M | logical | FALSE | TRUE, FALSE | - |
max_candidates | integer | 100 | \([1, \infty)\) | |
min_density | integer | 2 | \([1, \infty)\) | |
N | integer | 2 | \([1, \infty)\) | |
num_slots | integer | 1 | \([1, \infty)\) | |
O | logical | FALSE | TRUE, FALSE | - |
periodic_pruning | integer | 10000 | \([1, \infty)\) | |
S | integer | 10 | \([0, \infty)\) | |
t2 | numeric | -1 | \((-\infty, \infty)\) | |
t1 | numeric | -1.5 | \((-\infty, \infty)\) | |
V | logical | FALSE | TRUE, FALSE | - |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
References
Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76--77.
Forgy, W E (1965). “Cluster analysis of multivariate data: efficiency versus interpretability of classifications.” Biometrics, 21, 768--769.
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.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.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
-> LearnerClustSimpleKMeans
Examples
if (requireNamespace("RWeka")) {
learner = mlr3::lrn("clust.SimpleKMeans")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustSimpleKMeans:clust.SimpleKMeans>: K-Means (Weka)
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, RWeka
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "A" "C" "fast"
#> [4] "I" "init" "M"
#> [7] "max_candidates" "min_density" "N"
#> [10] "num_slots" "O" "periodic_pruning"
#> [13] "S" "t2" "t1"
#> [16] "V" "output_debug_info"