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dc.contributor.authorDomínguez Olmedo, Juan Luis 
dc.contributor.authorMata Vázquez, Jacinto 
dc.contributor.authorPachón Álvarez, Victoria 
dc.contributor.authorMaña López, Manuel Jesús 
dc.date.accessioned2018-10-23T07:38:51Z
dc.date.available2018-10-23T07:38:51Z
dc.date.issued2012
dc.identifier.citationDomínguez-Olmedo, J. L., Mata, J., Pachón, V., & Maña López, M.J. (2012). Rule extraction from medical data without discretization of numerical attributes. En: Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, 397-400. DOI 10.5220/0003784603970400es_ES
dc.identifier.isbn978-989-8425-88-1
dc.identifier.urihttp://hdl.handle.net/10272/15427
dc.description.abstractAssociation rule mining is a popular technique used to find associations between attributes in a dataset. When using deterministic algorithms, if the attributes have numerical values the usual approach is to discretize them defining proper intervals. But the discretization can notably affect the quality of the rules generated. This work presents a method based on a deterministic exploration of the interval search space without a previous discretization of the numerical attributes. It has been applied to medical data from an atherosclerosis study. The quality of the obtained rules seems to support this method as a valid alternative for this kind of rule extraction.es_ES
dc.language.isoenges_ES
dc.publisherScience and Technology Publicationses_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherData mininges_ES
dc.subject.otherAssociation ruleses_ES
dc.subject.otherAtherosclerosis dataes_ES
dc.titleRule extraction from medical data without discretization of numerical attributeses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.identifier.doi10.5220/0003784603970400
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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