In tidyclust: A Common API to Clustering

r descr_models("gm_clust", "mclust")

Tuning Parameters

defaults <- 
  tibble::tibble(tidyclust = c("num_clusters", "circular", "zero_covariance", "shared_orientation", "shared_shape", "shared_size"),
                 default = c("no default", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"))

param <-
 gm_clust() %>% 
  set_engine("mclust") %>% 
  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)

gm_clust(num_clusters = 3, circular = FALSE, zero_covariance = FALSE) %>% 
  set_engine("mclust") %>% 
  set_mode("partition") %>% 
  translate_tidyclust()

Preprocessing requirements

Gaussian Mixture Models should be fit with only quantitative predictors and without any categorical predictors. No scaling is required since the variance-covariance matrices of the Gaussian distributions account for the unequal variances between predictors and their covariances.

References

Banfield, J. D., & Raftery, A. E. (1993). Model-Based Gaussian and Non-Gaussian Clustering. Biometrics, 49(3), 803. doi: 10.2307/2532201
Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781–793. doi: 10.1016/0031-3203(94)00125-6
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm.
McNicholas, P. D. (2016). Model-Based clustering. Journal of Classification, 33(3), 331–373. https://link.springer.com/article/10.1007/s00357-016-9211-9
Scrucca, L., Fop, M., Murphy, T., Brendan, & Raftery, A., E. (2016). Mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. The R Journal, 8(1), 289. https://journal.r-project.org/articles/RJ-2016-021/index.html
Scrucca, L., Fraley, C., Murphy, T. B., & Raftery, A. E. (2023). Model-based clustering, classification, and density estimation using mclust in R. Chapman; Hall/CRC. https: //doi.org/10.1201/9781003277965