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Silvestre, C. V., Cardoso, M. G. M. S. & Figueiredo, M.A.T. (2022). An MML embedded approach for estimating the number of clusters. 17th conference of the International Federation of Classification Societies .

C. V. Silvestre et al.,  "An MML embedded approach for estimating the number of clusters", in 17th conference of the Int. Federation of Classification Societies , Porto, 2022

@misc{silvestre2022_1734875400966,
	author = "Silvestre, C. V. and Cardoso, M. G. M. S. and Figueiredo, M.A.T.",
	title = "An MML embedded approach for estimating the number of clusters",
	year = "2022",
	url = "https://ifcs2022.fep.up.pt/"
}

TY  - CPAPER
TI  - An MML embedded approach for estimating the number of clusters
T2  - 17th conference of the International Federation of Classification Societies 
AU  - Silvestre, C. V.
AU  - Cardoso, M. G. M. S.
AU  - Figueiredo, M.A.T.
PY  - 2022
CY  - Porto
UR  - https://ifcs2022.fep.up.pt/
AB  - Assuming that the data originate from a finite mixture of multinomial
distributions, we study the performance of an integrated Expectation Maximization
(EM) algorithm considering Minimum Message Length (MML) criterion to select
the number of mixture components. The referred EM-MML approach, rather than
selecting one among a set of pre-estimated candidate models (which requires running
EM several times), seamlessly integrates estimation and model selection in a
single algorithm. Comparisons are provided with EM combined with well-known
information criteria – e.g. the Bayesian information Criterion.We resort to synthetic
data examples and a real application. The EM-MML computation time is a clear advantage
of this method; also, the real data solution it provides is more parsimonious,
which reduces the risk of model order overestimation and improves interpretability.
ER  -