In EmilHvitfeldt/celery: A Common API to Clustering

r descr_models("k_means", "klaR")

Tuning Parameters

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

param <-
 k_means() %>% 
  set_engine("klaR") %>% 
  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("klaR") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

Preprocessing requirements

Only categorical variables are accepted, along with numerics with few unique values.

References

• Huang, Z. (1997) A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. in KDD: Techniques and Applications (H. Lu, H. Motoda and H. Luu, Eds.), pp. 21-34, World Scientific, Singapore.

• MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281-297. Berkeley, CA: University of California Press.

EmilHvitfeldt/celery documentation built on Jan. 31, 2025, 7:04 p.m.