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Hierarchical (Agglomerative) Clustering

Source: R/hier_clust.R
hier_clust.Rd

hier_clust() defines a model that fits clusters based on a distance-based dendrogram

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

Usage

hier_clust(
  mode = "partition",
  engine = "stats",
  num_clusters = NULL,
  cut_height = NULL,
  linkage_method = "complete",
  dist_fun = NULL
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "partition".

engine

A single character string specifying what computational engine to use for fitting. Possible engines are listed below. The default for this model is "stats".

num_clusters

Positive integer, number of clusters in model (optional).

cut_height

Positive double, height at which to cut dendrogram to obtain cluster assignments (only used if num_clusters is NULL)

linkage_method

the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

dist_fun

A function for calculating the distance between observations. Defaults to philentropy::distance which supports numerous distance metrics via its method argument. The function should accept a matrix or data frame and return a square numeric matrix or an object coercible to one via stats::as.dist(). See silhouette() for further details.

Details

What does it mean to predict?

To predict the cluster assignment for a new observation, we find the closest cluster. How we measure “closeness” is dependent on the specified type of linkage in the model:

  • single linkage: The new observation is assigned to the same cluster as its nearest observation from the training data.

  • complete linkage: The new observation is assigned to the cluster with the smallest maximum distances between training observations and the new observation.

  • average linkage: The new observation is assigned to the cluster with the smallest average distances between training observations and the new observation.

  • centroid method: The new observation is assigned to the cluster with the closest centroid, as in prediction for k_means.

Examples

# Show all engines
modelenv::get_from_env("hier_clust")
#> # A tibble: 1 × 2
#>   engine mode     
#>   <chr>  <chr>    
#> 1 stats  partition

hier_clust()
#> Hierarchical Clustering Specification (partition)
#> 
#> Main Arguments:
#>   linkage_method = complete
#> 
#> Computational engine: stats 
#>