Calculates Sum of Squared Error in each cluster

Calculates Sum of Squared Error in each cluster

Usage

sse_within(
  object,
  new_data = NULL,
  dist_fun = function(x, y) {
     philentropy::dist_many_many(x, y, method =
    "euclidean")
 }
)

Arguments

object

A fitted kmeans tidyclust model

new_data

A dataset to predict on. If NULL, uses trained clustering.

dist_fun

A function for calculating distances to centroids. Defaults to Euclidean distance on processed data.

Value

A tibble with two columns, the cluster name and the SSE within that cluster.

Details

sse_within_total() is the corresponding cluster metric function that returns the sum of the values given by sse_within().

Examples

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

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

sse_within(kmeans_fit)
#> # A tibble: 5 × 3
#>   .cluster     wss n_members
#>   <fct>      <dbl>     <int>
#> 1 Cluster_1  5808.         7
#> 2 Cluster_2  8945.         9
#> 3 Cluster_3   563.         2
#> 4 Cluster_4  6816.         6
#> 5 Cluster_5 42070.         8