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Extract cluster assignments from model

Source: R/extract_cluster_assignment.R
extract_cluster_assignment.Rd

When applied to a fitted cluster specification, returns a tibble with cluster assignments of the data used to train the model.

Usage

extract_cluster_assignment(object, ...)

Arguments

object

An fitted cluster_spec object.

...

Other arguments passed to methods. Using the prefix allows you to change the prefix in the levels of the factor levels.

Value

A tibble::tibble() with 1 column named .cluster. This tibble will correspond the the training data set.

Details

Some model types such as K-means as seen in k_means() stores the cluster assignments in the object itself. leading the use of this function to act as an simple extract. Other model types such as Hierarchical (Agglomerative) Clustering as seen in hier_clust(), are fit in such a way that the number of clusters can be determined at any time after the fit. Setting the num_clusters or cut_height in this function will be used to determine the clustering when reported.

The ordering of the clusters is such that the first observation in the training data set will be in cluster 1, the next observation that doesn't belong to cluster 1 will be in cluster 2, and so on and forth. As the ordering of clustering doesn't matter, this is done to avoid identical sets of clustering having different labels if fit multiple times.

extract_cluster_assignment() is a part of a trio of functions doing similar things:

  • extract_cluster_assignment() returns the cluster assignments of the training observations

  • extract_centroids() returns the location of the centroids

  • predict() returns the cluster a new observation belongs to

See also

extract_centroids() predict.cluster_fit()

Examples

kmeans_spec <- k_means(num_clusters = 5) %>%
  set_engine("stats")

kmeans_fit <- fit(kmeans_spec, ~., mtcars)

kmeans_fit %>%
  extract_cluster_assignment()
#> # A tibble: 32 × 1
#>    .cluster 
#>    <fct>    
#>  1 Cluster_1
#>  2 Cluster_1
#>  3 Cluster_1
#>  4 Cluster_2
#>  5 Cluster_3
#>  6 Cluster_2
#>  7 Cluster_4
#>  8 Cluster_1
#>  9 Cluster_1
#> 10 Cluster_1
#> # ℹ 22 more rows

kmeans_fit %>%
  extract_cluster_assignment(prefix = "C_")
#> # A tibble: 32 × 1
#>    .cluster
#>    <fct>   
#>  1 C_1     
#>  2 C_1     
#>  3 C_1     
#>  4 C_2     
#>  5 C_3     
#>  6 C_2     
#>  7 C_4     
#>  8 C_1     
#>  9 C_1     
#> 10 C_1     
#> # ℹ 22 more rows

# Some models such as `hier_clust()` fits in such a way that you can specify
# the number of clusters after the model is fit
hclust_spec <- hier_clust() %>%
  set_engine("stats")

hclust_fit <- fit(hclust_spec, ~., mtcars)

hclust_fit %>%
  extract_cluster_assignment(num_clusters = 2)
#> # A tibble: 32 × 1
#>    .cluster 
#>    <fct>    
#>  1 Cluster_1
#>  2 Cluster_1
#>  3 Cluster_1
#>  4 Cluster_1
#>  5 Cluster_2
#>  6 Cluster_1
#>  7 Cluster_2
#>  8 Cluster_1
#>  9 Cluster_1
#> 10 Cluster_1
#> # ℹ 22 more rows

hclust_fit %>%
  extract_cluster_assignment(cut_height = 250)
#> # A tibble: 32 × 1
#>    .cluster 
#>    <fct>    
#>  1 Cluster_1
#>  2 Cluster_1
#>  3 Cluster_1
#>  4 Cluster_2
#>  5 Cluster_3
#>  6 Cluster_2
#>  7 Cluster_3
#>  8 Cluster_1
#>  9 Cluster_1
#> 10 Cluster_1
#> # ℹ 22 more rows