Extending RCA algorithm to consider ternary relations

cic.institucionOrigenLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.lugarDesarrolloLaboratorio de Investigación y Formación en Informática Avanzada (LIFIA)
cic.parentTypeObjeto de conferencia
dc.date.accessioned2022-08-01T12:52:20Z
dc.date.available2022-08-01T12:52:20Z
dc.identifier.urihttps://digital.cic.gba.gob.ar/handle/11746/11617
dc.titleExtending RCA algorithm to consider ternary relationsen
dc.typeDocumento de conferenciaes
dcterms.abstractRelational Concept Analysis (RCA) is a multirelational data mining method that aims to extract knowledge from multiple formal contexts (i.e., objects, attributes, and a binary relation between them) and the relations between them. One of the problems RCA has is the lack of the possibility of extracting knowledge directly from data that is represented with ternary relations. While there are some existing solutions towards this problem, either they require complex preprocessing of the input data, or they lose some capabilities of RCA such as the different meanings of the relations between concepts (∃, ∀, etc). In this work, we present an intuitive extension to RCA to be able to use it with data directly represented with ternary relations. As an example of its usage, we apply it to a dataset called Knomana which includes ternary relationsen
dcterms.creator.authorLeutwyler, Nicolás
dcterms.creator.authorLezoche, Mario
dcterms.creator.authorPanetto, Hervé
dcterms.creator.authorTorres, Diego
dcterms.descriptionPublicado en : ISOS Conference Proceedings Series.
dcterms.identifier.isbn978-86-85525-24-7
dcterms.isPartOf.seriesICIST 2022 - 12th International Conference on Information Society and Technology (Serbia, 13 al 16 de mayo de 2022)
dcterms.issued2022
dcterms.subjectKnowledge representationen
dcterms.subjectFormal Concept Analysisen
dcterms.subjectRelational Concept Analysisen
dcterms.subjectTernary relationsen
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