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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Bibliografische gegevens
Hoofdauteurs:	Gámez Martín, José Antonio, Rumí, Rafael, Salmerón Cerdán, Antonio
Formaat:	info:eu-repo/semantics/report
Taal:	English
Gepubliceerd in:	2012
Online toegang:	http://hdl.handle.net/10835/1555

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