A LearnerClust for Mean Shift clustering implemented in LPCM::ms()
.
There is no predict method for LPCM::ms()
, so the method
returns cluster labels for the 'training' data.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("clust.meanshift")
mlr_learnerslrn("clust.meanshift")
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, LPCM
Parameters
Id | Type | Default | Range |
h | untyped | - | - |
subset | untyped | - | - |
scaled | integer | 1 | \([0, \infty)\) |
iter | integer | 200 | \([1, \infty)\) |
thr | numeric | 0.01 | \((-\infty, \infty)\) |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustMeanShift
Examples
if (requireNamespace("LPCM")) {
learner = mlr3::lrn("clust.meanshift")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustMeanShift:clust.meanshift>: Mean Shift Clustering
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3cluster, LPCM
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional
#> [1] "h" "subset" "scaled" "iter" "thr"