EGMM: An evidential version of the Gaussian mixture model for clustering

The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new model-based clustering algorithm, called EGMM (evidential GMM), in the theoretical framework of belief functions to better c...

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Vydané v:	Applied soft computing Ročník 129; s. 109619
Hlavní autori:	Jiao, Lianmeng, Denœux, Thierry, Liu, Zhun-ga, Pan, Quan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2022 Elsevier
Predmet:	Artificial Intelligence Belief function theory Computer Science Evidential partition Expectation–Maximization Gaussian mixture model Model-based clustering Belief function theory Evidential partition Gaussian mixture model Model-based clustering Expectation–Maximization evidential partition belief function theory expectation-maximization model-based clustering
ISSN:	1568-4946, 1872-9681
On-line prístup:	Získať plný text
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