An MML embedded approach for estimating the number of clusters

Silvestre, Cláudia; Cardoso, Maria Margarida G. M. S.; Figueiredo, Mário

Publication

An MML embedded approach for estimating the number of clusters

2023-12-08Book part

dc.contributor.author	Silvestre, Cláudia
dc.contributor.author	Cardoso, Maria Margarida G. M. S.
dc.contributor.author	Figueiredo, Mário
dc.date.accessioned	2023-12-13T11:45:56Z
dc.date.available	2023-12-13T11:45:56Z
dc.date.issued	2023-12-08
dc.description.abstract	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	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Silvestre, C., Cardoso, M.G.M.S., Figueiredo, M. (2023). An MML embedded approach for estimating the number of clusters. In P. Brito, J.G. Dias, B. Lausen, A. Montanari, & R. Nugent (eds), Classification and data science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization (pp. 353-361), Springer. https://doi.org/10.1007/978-3-031-09034-9_38	pt_PT
dc.identifier.doi	https://doi.org/10.1007/978-3-031-09034-9_38	pt_PT
dc.identifier.isbn	978-3-031-09034-9
dc.identifier.isbn	978-3-031-09033-2 (print)
dc.identifier.uri	http://hdl.handle.net/10400.21/16694
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer	pt_PT
dc.relation.publisherversion	https://link.springer.com/chapter/10.1007/978-3-031-09034-9_38	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/	pt_PT
dc.subject	Finite mixture model	pt_PT
dc.subject	EM algorithm	pt_PT
dc.subject	Model selection	pt_PT
dc.subject	Minimum message length	pt_PT
dc.subject	Categorical data	pt_PT
dc.title	An MML embedded approach for estimating the number of clusters	pt_PT
dc.type	book part
dspace.entity.type	Publication
oaire.citation.conferencePlace	Cham	pt_PT
oaire.citation.endPage	361	pt_PT
oaire.citation.startPage	353	pt_PT
oaire.citation.title	Classification and data science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization	pt_PT
person.familyName	Silvestre
person.familyName	Cardoso
person.givenName	Cláudia
person.givenName	Maria Margarida
person.identifier.ciencia-id	DA12-EF3F-C7CD
person.identifier.ciencia-id	3E1B-1DAD-9287
person.identifier.orcid	0000-0002-8850-4304
person.identifier.orcid	0000-0001-6239-7283
person.identifier.scopus-author-id	21233265300
rcaap.rights	openAccess	pt_PT
rcaap.type	bookPart	pt_PT
relation.isAuthorOfPublication	08fbc1bf-3387-4137-8c03-c4664dd43375
relation.isAuthorOfPublication	069c85c4-ebe3-4293-b88a-dd066bc288de
relation.isAuthorOfPublication.latestForDiscovery	069c85c4-ebe3-4293-b88a-dd066bc288de