details_gm_clust_mclust: Gaussian Mixture Model (GMM) via mclust

In tidyclust: A Common API to Clustering

Gaussian Mixture Model (GMM) via mclust

Description

gm_clust() creates GMM model.

Details

For this engine, there is a single mode: partition

Tuning Parameters

This model has 6 tuning parameters:

• num_clusters: # Clusters (type: integer, default: no default)

• circular: Circular MVG (type: logical, default: TRUE)

• zero_covariance: Zero Covariance (type: logical, default: TRUE)

• shared_orientation: Shared Orientation (type: logical, default: TRUE)

• shared_shape: Shared Shape (type: logical, default: TRUE)

• shared_size: Shared Size (type: logical, default: TRUE)

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()

## GMM Clustering Specification (partition)
## 
## Main Arguments:
##   num_clusters = 3
##   circular = FALSE
##   zero_covariance = FALSE
##   shared_orientation = TRUE
##   shared_shape = TRUE
##   shared_size = TRUE
## 
## Computational engine: mclust 
## 
## Model fit template:
## tidyclust::.gm_clust_fit_mclust(x = missing_arg(), num_clusters = missing_arg(), 
##     circular = missing_arg(), zero_covariance = missing_arg(), 
##     shared_orientation = missing_arg(), shared_shape = missing_arg(), 
##     shared_size = missing_arg(), num_clusters = 3, circular = FALSE, 
##     zero_covariance = FALSE, shared_orientation = TRUE, shared_shape = TRUE, 
##     shared_size = TRUE)

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

tidyclust documentation built on June 20, 2026, 9:08 a.m.