mlr_learners_clust.fanny | R Documentation |
A LearnerClust for fuzzy clustering implemented in cluster::fanny()
.
cluster::fanny()
doesn't have a default value for the number of clusters.
Therefore, the k
parameter which corresponds to the number
of clusters here is set to 2 by default.
The predict method copies cluster assignments and memberships
generated for train data. The predict does not work 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$get("clust.fanny") lrn("clust.fanny")
Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster
Id | Type | Default | Levels | Range |
k | integer | - | [1, \infty) |
|
memb.exp | numeric | 2 | [1, \infty) |
|
metric | character | euclidean | euclidean, manhattan, SqEuclidean | - |
stand | logical | FALSE | TRUE, FALSE | - |
maxit | integer | 500 | [0, \infty) |
|
tol | numeric | 1e-15 | [0, \infty) |
|
trace.lev | integer | 0 | [0, \infty) |
|
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustFanny
new()
Creates a new instance of this R6 class.
LearnerClustFanny$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustFanny$clone(deep = FALSE)
deep
Whether to make a deep clone.
Kaufman, Leonard, Rousseeuw, J P (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3extralearners for more learners.
Dictionary of Learners: mlr3::mlr_learners
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).
mlr3pipelines to combine learners with pre- and postprocessing steps.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
mlr_learners_clust.MBatchKMeans
,
mlr_learners_clust.SimpleKMeans
,
mlr_learners_clust.agnes
,
mlr_learners_clust.ap
,
mlr_learners_clust.bico
,
mlr_learners_clust.birch
,
mlr_learners_clust.cmeans
,
mlr_learners_clust.cobweb
,
mlr_learners_clust.dbscan
,
mlr_learners_clust.dbscan_fpc
,
mlr_learners_clust.diana
,
mlr_learners_clust.em
,
mlr_learners_clust.featureless
,
mlr_learners_clust.ff
,
mlr_learners_clust.hclust
,
mlr_learners_clust.hdbscan
,
mlr_learners_clust.kkmeans
,
mlr_learners_clust.kmeans
,
mlr_learners_clust.mclust
,
mlr_learners_clust.meanshift
,
mlr_learners_clust.optics
,
mlr_learners_clust.pam
,
mlr_learners_clust.xmeans
if (requireNamespace("cluster")) {
learner = mlr3::lrn("clust.fanny")
print(learner)
# available parameters:
learner$param_set$ids()
}
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