GMMinit-package: Optimal Initial Value for Gaussian Mixture Model

Description

Provides an approach to compute an optimal initial value for the Expectation-Maximization (EM) algorithm when fitting a Gaussian Mixture Model (GMM). This ensures better convergence and improved model fitting.

Arguments

Author

Jing Li jli178@crimson.ua.edu [aut, cre]
Yana Melnykov ymelnykov@ua.edu [aut]

Details

Package:	GMMinit
Type:	Package
Version:	1.0.0
Date:	2026-01-20
License:	GPL (>= 2)
LazyLoad:	no

This package includes functions for:

Computing an optimal initialization for GMM.
Running the Expectation-Maximization (EM) algorithm.

References

Michael, S., & Melnykov, V. (2016). An effective strategy for initializing the EM algorithm in finite mixture models. Advances in Data Analysis and Classification, 10(4), 563–583.

Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611-631. [mclust]

Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 100-108. [k-means]

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-22. [EM Algorithm]

Celeux, G., & Govaert, G. (1992). A classification EM algorithm for clustering and two stochastic versions. Computational Statistics & Data Analysis, 14(3), 315-332. [CEM & SEM]

McLachlan, G., & Peel, D. (2000). Finite Mixture Models. John Wiley & Sons. [General EM & GMM]

Biernacki, C., Celeux, G., & Govaert, G. (2003). Choosing starting values for the EM algorithm in Gaussian mixture models. Computational Statistics & Data Analysis, 41(3-4), 561-575. [Alternative EM Initializations]

Examples

Run this code

# Generate sample data
set.seed(123)
data <- matrix(rnorm(100 * 2), ncol = 2)

# Compute optimal initialization
library(GMMinit)
init <- getInit(data, k = 2, method = "Random")
print(init)