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.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("clust.fanny")
mlr_learnerslrn("clust.fanny")
Meta Information
Task type: “clust”
Predict Types: “partition”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster
Parameters
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 | \([0, \infty)\) |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustFanny
Examples
if (requireNamespace("cluster")) {
learner = mlr3::lrn("clust.fanny")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustFanny:clust.fanny>: Fuzzy Analysis Clustering
#> * Model: -
#> * Parameters: k=2
#> * Packages: mlr3, mlr3cluster, cluster
#> * Predict Types: [partition], prob
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, fuzzy, partitional
#> [1] "k" "memb.exp" "metric" "stand" "maxit" "tol"
#> [7] "trace.lev"