Agglomerative Hierarchical Clustering Learner
Source:R/LearnerClustAgnes.R
mlr_learners_clust.agnes.Rd
A LearnerClust for agglomerative hierarchical clustering implemented in cluster::agnes()
.
The predict method uses stats::cutree()
which cuts the tree resulting from
hierarchical clustering into specified number of groups (see parameter k
).
The default number for k
is 2.
Dictionary
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn()
:
$get("clust.agnes")
mlr_learnerslrn("clust.agnes")
Meta Information
Task type: “clust”
Predict Types: “partition”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3cluster, cluster
Parameters
Id | Type | Default | Levels | Range |
metric | character | euclidean | euclidean, manhattan | - |
stand | logical | FALSE | TRUE, FALSE | - |
method | character | average | average, single, complete, ward, weighted, flexible, gaverage | - |
trace.lev | integer | 0 | \([0, \infty)\) | |
k | integer | 2 | \([1, \infty)\) | |
par.method | untyped | - | - |
Super classes
mlr3::Learner
-> mlr3cluster::LearnerClust
-> LearnerClustAgnes
Examples
if (requireNamespace("cluster")) {
learner = mlr3::lrn("clust.agnes")
print(learner)
# available parameters:
learner$param_set$ids()
}
#> <LearnerClustAgnes:clust.agnes>: Agglomerative Hierarchical Clustering
#> * Model: -
#> * Parameters: k=2
#> * Packages: mlr3, mlr3cluster, cluster
#> * Predict Types: [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, hierarchical
#> [1] "metric" "stand" "method" "trace.lev" "k"
#> [6] "par.method"