KDSM methodology for knowledge discovery from a domain where repeated very short serial measures with a blocking factor are present

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/97383

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.contributor	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor	Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.author	Rodas, Jorge
dc.contributor.author	Alvarado, Gabriela
dc.contributor.author	Gibert, Karina
dc.contributor.author	Rojo, Emilio
dc.contributor.author	Cortés García, Claudio Ulises
dc.date	2003-02
dc.identifier.citation	Rodas, J., Alvarado, G., Gibert, Karina, Rojo, E., Cortes, C. "KDSM methodology for knowledge discovery from a domain where repeated very short serial measures with a blocking factor are present". 2003.
dc.identifier.uri	http://hdl.handle.net/2117/97383
dc.language.iso	eng
dc.relation	LSI-03-7-R
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica
dc.subject	KDSM
dc.subject	Knowledge discovery in serial measurement
dc.title	KDSM methodology for knowledge discovery from a domain where repeated very short serial measures with a blocking factor are present
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/report
dc.description.abstract	Objective -- To present the Knowledge Discovery in Serial Measurement (KDSM) methodology for analyzing repeated very short serial measures with a blocking factor. Method -- An application to labor the domain is described using KDSM. Results -- Novel knowledge about labor domain's behavior was obtained once KDSM was applied to this specific domain. Conclusion -- KDSM has shown that important information has been missed, especially when the kind of data explained in this paper is analyzed with common statistical methods and artificial intelligence techniques, independently employed.

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