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. https://www.jstor.org/stable/2532201
Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781–793. https://www.sciencedirect.com/science/article/pii/0031320394001256
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