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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Bibliographic Details
Published in:	Applied soft computing Vol. 129; p. 109619
Main Authors:	Jiao, Lianmeng, Denœux, Thierry, Liu, Zhun-ga, Pan, Quan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.11.2022 Elsevier
Subjects:	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
Online Access:	Get full text
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