我希望使用Tidymodels为K均值聚类选择最佳的K值。我正在探索使用工作流集来提供大量预处理方法，我希望在选择K值时比较它们的性能。
我尝试将this tutorial与this one结合起来，讨论如何使用工作流集来比较模型。
我正在使用mtcars数据，我一直停留在超参数调优部分，在那里我试图收集调优结果。
我被卡在了一个工作流集所在的部分，并将其传递给tune_cluster。我收到了以下错误：
调整群集（）: The first argument to [tune_cluster()] should be either a model or workflow.

# INSTALL PACKAGES
pacman::p_load(tidyverse, tidymodels, tidyclust, janitor, ClusterR, knitr, moments, visdat, skimr, DescTools)

mtcars <- mtcars %>%
  mutate(
    `am` = factor(`am`, labels = c(`0` = "auto", `1` = "man")),
    `vs` = factor(`vs`, labels = c(`0` = "V-shaped", `1` = "straight")),
    `cyl` = factor(`cyl`),
    `gear` = factor(`gear`),
    `carb` = factor(`carb`)
  )

# SET UP 10 FOLD CROSS VALIDATION
mtcars_cv <- vfold_cv(mtcars, v = 10)

# SET SEED FOR REPRODUCABILITY
set.seed(123)

# EDA ---------------------------------------------------------------------

#skimr::skim(mtcars)

#DescTools::Desc(mtcars)

# MODEL SPEC --------------------------------------------------------------

kmeans_spec <- k_means(num_clusters = tune())

# PREPROCESSING RECIPES ---------------------------------------------------

rec1 <- recipe(~., data = mtcars) %>%
  step_dummy(all_nominal_predictors()) %>%
  step_zv(all_predictors()) %>%
  step_normalize(all_numeric_predictors())

rec2 <- recipe(~., data = mtcars) %>%
  step_novel(all_nominal()) %>%
  step_dummy(all_nominal()) %>%
  step_zv(all_predictors()) %>%
  step_normalize(all_predictors()) %>%
  step_pca(all_predictors(), num_comp = 2)

rec3 <- recipe(~., data = mtcars) %>%
  step_log(all_numeric_predictors()) %>%
  step_center(all_numeric_predictors()) %>%
  step_scale(all_numeric_predictors())

clust_num_grid <- grid_regular(num_clusters(),
  levels = 10
)

# WORKFLOW ----------------------------------------------------------------

wf_set <- workflow_set(
  preproc = list(rec1, rec2, rec3),
  models = list(kmeans_spec)
)

# TUNE HYPER-PARAMETERS ---------------------------------------------------

tune_results <- wf_set %>%
  workflow_map(
    resamples = mtcars_cv,
    grid = clust_num_grid
  ) %>%
  tune_cluster(
    resamples = mtcars_cv,
    grid = clust_num_grid,
    metrics = cluster_metric_set(sse_within_total, sse_total, sse_ratio),
    control = tune::control_grid(save_pred = TRUE, extract = identity)
  )

best_wf <- tune_results %>%
  select_best("sse_ratio")

如能帮助解决这一问题，我们将不胜感激。