Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleInternational Conference on Neural Information Processing (ICONIP)es_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.endPage144es_ES
UDC.grupoInvLaboratorio Interdisciplinar de Aplicacións da Intelixencia Artificial (LIA2)es_ES
UDC.journalTitleLecture Notes in Computer Sciencees_ES
UDC.startPage137es_ES
UDC.volume2016es_ES
dc.contributor.authorÁlvarez, M. A.
dc.contributor.authorDafonte, Carlos
dc.contributor.authorGarabato, D.
dc.contributor.authorManteiga, Minia
dc.date.accessioned2024-02-05T09:01:46Z
dc.date.available2024-02-05T09:01:46Z
dc.date.issued2016
dc.descriptionThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-319-46681-1_17es_ES
dc.descriptionVersión final aceptada de: Álvarez, M.A., Dafonte, C., Garabato, D., Manteiga, M. (2016). Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_17es_ES
dc.description.abstract[Abstract]: A billion stars: this is the approximate amount of visible objects estimated to be observed by the Gaia satellite, representing roughly 1 % of the objects in the Galaxy. It constitutes the biggest amount of data gathered to date: by the end of the mission, the data archive will exceed 1 Petabyte. Now, in order to process this data, the Gaia mission conceived the Data Processing and Analysis Consortium, which will apply data mining techniques such as Self-Organizing Maps. This paper shows a useful technique for source clustering, focusing on the development of an advanced visualization tool based on this technique.es_ES
dc.identifier.citationÁlvarez, M.A., Dafonte, C., Garabato, D., Manteiga, M. (2016). Analysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releases. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_17es_ES
dc.identifier.doi10.1007/978-3-319-46681-1_17
dc.identifier.urihttp://hdl.handle.net/2183/35383
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-319-46681-1_17
dc.relation.urihttp://dx.doi.org/10.1007/978-3-319-46681-1_17es_ES
dc.rights© Springer International Publishing AG 2016es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectGaia missiones_ES
dc.subjectEuropean Space Agencyes_ES
dc.subjectData mininges_ES
dc.subjectArtificial intelligencees_ES
dc.subjectSelf-Organizing Maps visualizationses_ES
dc.titleAnalysis and Knowledge Discovery by Means of Self-Organizing Maps for Gaia Data Releaseses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
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relation.isAuthorOfPublicationc3c2021f-0b5d-408f-afff-ec09ab5eaeee
relation.isAuthorOfPublication1a431829-71d0-44aa-a001-8d2984c3b413
relation.isAuthorOfPublicationac152b53-40d7-47ed-a5d2-036b0374adb7
relation.isAuthorOfPublication.latestForDiscovery66ff8e1a-a945-4d02-bc89-7fa42c7947fe

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