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dc.contributor.authorSànchez-Marrè, Miquel
dc.contributor.authorGómez Villamor, Sergio
dc.contributor.authorTeixidò, F
dc.contributor.authorGibert, Karina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.identifier.citationSànchez-Marrè, M., Gómez, S., Teixidò, F., Gibert, Karina. "Tècniques de feature weighting per casos no supervisats: Implementació a GESCONDA". 2006.
dc.description.abstractFeature selection and feature weighting methods in supervised domains have been thoroughly discussed in the literature. On the other hand, very little work has been done for unsupervised domains, probably due to the assumed hypothesis that their performance would necessary be substantially worse than the supervised method performance. One method found in the literature, in addition to the new methods proposed are detailed in this paper. The methods have been tested and compared in a data base coming from a Wastewater Treatment plant, with good results. Also, the integration of the new software into the GESCONDA tool is detailed.
dc.format.extent32 p.
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.otherUnsupervised feature weighting
dc.subject.otherMachine learning
dc.subject.otherData mining
dc.titleTècniques de feature weighting per casos no supervisats: Implementació a GESCONDA
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. DAMA-UPC - Data Management Group
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
upcommons.citation.authorSànchez-Marrè, M.; Gómez, S.; Teixidò, F.; Gibert, Karina

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