Comprehensive validation metrics for a clustering result
Usage
calc_validation_metrics(clusters, data = NULL, dist_mat = NULL)
Arguments
- clusters
Vector of cluster assignments: numeric, factor or
character. A label of 0 marks noise, as tidy_dbscan
reports it: noise points are left out of every measure and counted in
n_noise.
- data
Original data frame (for WSS calculation). WSS is taken
over its numeric columns, so it needs at least one.
- dist_mat
Distance matrix (for silhouette)
Value
A single-row tibble with columns k, min_size,
max_size, avg_size, n_noise, and optionally
avg_silhouette, min_silhouette (if dist_mat
provided; NA for a single cluster), and total_wss (if
data provided).
Examples
# \donttest{
km <- kmeans(iris[, 1:4], centers = 3, nstart = 25)
d <- dist(iris[, 1:4])
metrics <- calc_validation_metrics(km$cluster, iris[, 1:4], d)
# }
