Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

In this paper we propose a naive Bayes model for unsupervised data clustering, where the class variable is hidden. The feature variables can be discrete or continuous, as the conditional distributions are represented as mixtures of truncated exponentials (MTEs). The number of classes is determined u...

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Dades bibliogràfiques
Autors principals:	Gámez Martín, José Antonio, Rumí, Rafael, Salmerón Cerdán, Antonio
Format:	info:eu-repo/semantics/report
Idioma:	English
Publicat:	2012
Accés en línia:	http://hdl.handle.net/10835/1555

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