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 Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
mlr_learners$get("clust.fanny") lrn("clust.fanny")
Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster
Id | Type | Default | Levels | Range |
k | integer | 2 | [1, \infty) |
|
memb.exp | numeric | 2 | [1, \infty) |
|
metric | character | euclidean | euclidean, manhattan, SqEuclidean | - |
stand | logical | FALSE | TRUE, FALSE | - |
maxit | integer | 500 | [0, \infty) |
|
tol | numeric | 1e-15 | [0, \infty) |
|
trace.lev | integer | 0 | [0, \infty) |
|
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustFanny
new()
Creates a new instance of this R6 class.
LearnerClustFanny$new()
clone()
The objects of this class are cloneable with this method.
LearnerClustFanny$clone(deep = FALSE)
deep
Whether to make a deep clone.
if (requireNamespace("cluster")) {
learner = mlr3::lrn("clust.fanny")
print(learner)
# available parameters:
learner$param_set$ids()
}
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