View source: R/extract_centroids.R
extract_centroids | R Documentation |
When applied to a fitted cluster specification, returns a tibble with cluster location. When such locations doesn't make sense for the model, a mean location is used.
extract_centroids(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 centroid
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.
Further more, some models like hier_clust()
, doesn't have a notion of
"centroids". The mean of the observation within each cluster assignment is
returned as the centroid.
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_centroids()
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 row for each centroid and their position.
.cluster
denotes the cluster name for the centroid. The remaining
variables match variables passed into model.
extract_cluster_assignment()
predict.cluster_fit()
set.seed(1234)
kmeans_spec <- k_means(num_clusters = 5) %>%
set_engine("stats")
kmeans_fit <- fit(kmeans_spec, ~., mtcars)
kmeans_fit %>%
extract_centroids()
# Some models such as `hier_clust()` fits in such a way that you can specify
# the number of clusters after the model is fit.
# A Hierarchical (Agglomerative) Clustering method doesn't technically have
# clusters, so the center of the observation within each cluster is returned
# instead.
hclust_spec <- hier_clust() %>%
set_engine("stats")
hclust_fit <- fit(hclust_spec, ~., mtcars)
hclust_fit %>%
extract_centroids(num_clusters = 2)
hclust_fit %>%
extract_centroids(cut_height = 250)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.