View source: R/extract_cluster_assignment.R
extract_cluster_assignment | R Documentation |
When applied to a fitted cluster specification, returns a tibble with cluster assignments of the data used to train the model.
extract_cluster_assignment(object, ...)
object |
An fitted |
... |
Other arguments passed to methods. Using the |
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
A tibble::tibble()
with 1 column named .cluster
. This tibble will
correspond the the training data set.
extract_centroids()
predict.cluster_fit()
kmeans_spec <- k_means(num_clusters = 5) %>%
set_engine("stats")
kmeans_fit <- fit(kmeans_spec, ~., mtcars)
kmeans_fit %>%
extract_cluster_assignment()
kmeans_fit %>%
extract_cluster_assignment(prefix = "C_")
# 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)
hclust_fit %>%
extract_cluster_assignment(cut_height = 250)
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