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...
| Հիմնական հեղինակներ: | , , |
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| Ձևաչափ: | info:eu-repo/semantics/report |
| Լեզու: | English |
| Հրապարակվել է: |
2012
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| Առցանց հասանելիություն: | http://hdl.handle.net/10835/1555 |
| _version_ | 1789406619970306048 |
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| author | Gámez Martín, José Antonio Rumí, Rafael Salmerón Cerdán, Antonio |
| author_facet | Gámez Martín, José Antonio Rumí, Rafael Salmerón Cerdán, Antonio |
| author_sort | Gámez Martín, José Antonio |
| collection | DSpace |
| description | 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 using the data augmentation algorithm. The proposed model is compared with the conditional Gaussian model for some real world and synthetic databases. |
| format | info:eu-repo/semantics/report |
| id | oai:repositorio.ual.es:10835-1555 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-15552023-04-12T19:40:17Z Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials Gámez Martín, José Antonio Rumí, Rafael Salmerón Cerdán, Antonio 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 using the data augmentation algorithm. The proposed model is compared with the conditional Gaussian model for some real world and synthetic databases. 2012-05-28T09:50:18Z 2012-05-28T09:50:18Z 2006 info:eu-repo/semantics/report Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM'06), pp. 123-132. http://hdl.handle.net/10835/1555 en info:eu-repo/semantics/openAccess Third European Workshop on Probabilistic Graphical Models (PGM'06) |
| spellingShingle | Gámez Martín, José Antonio Rumí, Rafael Salmerón Cerdán, Antonio Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials |
| title | Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials |
| title_full | Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials |
| title_fullStr | Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials |
| title_full_unstemmed | Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials |
| title_short | Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials |
| title_sort | unsupervised naive bayes for data clustering with mixtures of truncated exponentials |
| url | http://hdl.handle.net/10835/1555 |
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