mlr_learners_clust.xmeans | R Documentation |
A LearnerClust for X-means clustering implemented in RWeka::XMeans()
.
The predict method uses RWeka::predict.Weka_clusterer()
to compute the
cluster memberships for new data.
This mlr3::Learner can be instantiated via the dictionary mlr3::mlr_learners or with the associated sugar function mlr3::lrn()
:
mlr_learners$get("clust.xmeans") lrn("clust.xmeans")
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, RWeka
Id | Type | Default | Levels | Range |
B | numeric | 1 | [0, \infty) |
|
C | numeric | 0 | [0, \infty) |
|
D | untyped | "weka.core.EuclideanDistance" | - | |
H | integer | 4 | [1, \infty) |
|
I | integer | 1 | [1, \infty) |
|
J | integer | 1000 | [1, \infty) |
|
K | untyped | "" | - | |
L | integer | 2 | [1, \infty) |
|
M | integer | 1000 | [1, \infty) |
|
S | integer | 10 | [1, \infty) |
|
U | integer | 0 | [0, \infty) |
|
use_kdtree | logical | FALSE | TRUE, FALSE | - |
N | untyped | - | - | |
O | untyped | - | - | |
Y | untyped | - | - | |
output_debug_info | logical | FALSE | TRUE, FALSE | - |
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustXMeans
new()
Creates a new instance of this R6 class.
LearnerClustXMeans$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustXMeans$clone(deep = FALSE)
deep
Whether to make a deep clone.
Witten, H I, Frank, Eibe (2002). “Data mining: practical machine learning tools and techniques with Java implementations.” Acm Sigmod Record, 31(1), 76–77.
Pelleg, Dan, Moore, W A, others (2000). “X-means: Extending k-means with efficient estimation of the number of clusters.” In Icml, volume 1, 727–734.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3extralearners for more learners.
Dictionary of Learners: mlr3::mlr_learners
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_clust.MBatchKMeans
,
mlr_learners_clust.SimpleKMeans
,
mlr_learners_clust.agnes
,
mlr_learners_clust.ap
,
mlr_learners_clust.bico
,
mlr_learners_clust.birch
,
mlr_learners_clust.cmeans
,
mlr_learners_clust.cobweb
,
mlr_learners_clust.dbscan
,
mlr_learners_clust.dbscan_fpc
,
mlr_learners_clust.diana
,
mlr_learners_clust.em
,
mlr_learners_clust.fanny
,
mlr_learners_clust.featureless
,
mlr_learners_clust.ff
,
mlr_learners_clust.hclust
,
mlr_learners_clust.hdbscan
,
mlr_learners_clust.kkmeans
,
mlr_learners_clust.kmeans
,
mlr_learners_clust.mclust
,
mlr_learners_clust.meanshift
,
mlr_learners_clust.optics
,
mlr_learners_clust.pam
if (requireNamespace("RWeka")) {
learner = mlr3::lrn("clust.xmeans")
print(learner)
# available parameters:
learner$param_set$ids()
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.