Compute the sum of within-cluster SSE

Compute the sum of within-cluster SSE

Usage

sse_within_total(object, ...)

# S3 method for class 'cluster_spec'
sse_within_total(object, ...)

# S3 method for class 'cluster_fit'
sse_within_total(object, new_data = NULL, dist_fun = NULL, ...)

# S3 method for class 'workflow'
sse_within_total(object, new_data = NULL, dist_fun = NULL, ...)

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

Arguments

object

A fitted kmeans tidyclust model

...

Other arguments passed to methods.

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 3 columns; .metric, .estimator, and .estimate.

Details

Not to be confused with sse_within() that returns a tibble with within-cluster SSE, one row for each cluster.

See also

Other cluster metric: silhouette_avg(), sse_ratio(), sse_total()

Examples

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

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

sse_within_total(kmeans_fit)
#> # A tibble: 1 × 3
#>   .metric          .estimator .estimate
#>   <chr>            <chr>          <dbl>
#> 1 sse_within_total standard      53990.

sse_within_total_vec(kmeans_fit)
#> [1] 53990.34