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Luengo Sánchez, Sergio ORCID: https://orcid.org/0000-0001-8537-1879, Larrañaga Múgica, Pedro María
ORCID: https://orcid.org/0000-0002-1885-4501 and Bielza Lozoya, María Concepción
ORCID: https://orcid.org/0000-0001-7109-2668
(2016).
Hybrid Gaussian and von Mises model-based clustering.
In: "European Conference on Artificial Intelligence, ECAI 2016", 29 Aug-02 Sep 2016, La Haya, Holanda. ISBN 978-1-61499-671-2. pp. 855-862.
https://doi.org/10.3233/978-1-61499-672-9.
Title: | Hybrid Gaussian and von Mises model-based clustering |
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Author/s: |
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Item Type: | Presentation at Congress or Conference (Article) |
Event Title: | European Conference on Artificial Intelligence, ECAI 2016 |
Event Dates: | 29 Aug-02 Sep 2016 |
Event Location: | La Haya, Holanda |
Title of Book: | Frontiers in Artificial Intelligence and Applications |
Date: | 2016 |
ISBN: | 978-1-61499-671-2 |
Volume: | 285 |
Subjects: | |
Faculty: | E.T.S. de Ingenieros Informáticos (UPM) |
Department: | Inteligencia Artificial |
Creative Commons Licenses: | Recognition - No derivative works - Non commercial |
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Data collected about a phenomenon often measures its magnitude and direction. The most common approach to clustering this data assumes that directional data can be modeled as Gaussian. However, directional data has special properties that conventional statistics cannot handle. To deal with them, other approaches like the von Mises distribution must be applied. In this paper we present a new model based on mixtures of Bayesian networks to simultaneously cluster both linear and directional data.
Item ID: | 46643 |
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DC Identifier: | https://oa.upm.es/46643/ |
OAI Identifier: | oai:oa.upm.es:46643 |
DOI: | 10.3233/978-1-61499-672-9 |
Official URL: | http://ebooks.iospress.nl/volumearticle/44834 |
Deposited by: | Memoria Investigacion |
Deposited on: | 15 Mar 2018 11:19 |
Last Modified: | 30 Nov 2022 09:00 |