In tidyclust: A Common API to Clustering

r descr_models("k_means", "clustMixType")

Tuning Parameters

defaults <- 
  tibble::tibble(tidyclust = c("num_clusters"),
                 default = c("no default"))

param <-
 k_means() %>% 
  set_engine("clustMixType") %>% 
  set_mode("partition") %>% 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from tidyclust to the original package (partition)

k_means(num_clusters = integer(1)) %>% 
  set_engine("clustMixType") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

Preprocessing requirements

Both categorical and numeric predictors are required.

References

Szepannek, G. (2018): clustMixType: User-Friendly Clustering of Mixed-Type Data in R, The R Journal 10/2, 200-208, doi:10.32614/RJ-2018-048.
Aschenbruck, R., Szepannek, G., Wilhelm, A. (2022): Imputation Strategies for Clustering Mixed‑Type Data with Missing Values, Journal of Classification, doi:10.1007/s00357-022-09422-y.
Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304.