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dc.contributor.authorAlaminos, David
dc.contributor.authorFernandez, David
dc.contributor.authorGarcía-Lopera, Francisca 
dc.contributor.authorFernandez, Manuel Angel
dc.date.accessioned2018-07-31T10:20:36Z
dc.date.available2018-07-31T10:20:36Z
dc.date.created2018
dc.date.issued2018-07-31
dc.identifier.urihttps://hdl.handle.net/10630/16388
dc.description.abstractData mining techniques are capable of extracting valuable knowledge from large and variable databases. This work proposes a data mining method for municipal financial distress prediction. Using a new proxy of municipal financial situation and a sample of 128 Spanish municipalities, the empirical experiment obtained satisfactory results, which testifies to the viability and validity of the data mining method proposed for municipal financial distress prediction.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMinería de datos (Informática)en_US
dc.subject.otherFinancial distress predictionen_US
dc.subject.otherData miningen_US
dc.subject.otherMunicipalitiesen_US
dc.subject.otherIndividual classifiersen_US
dc.subject.otherLocal governmentsen_US
dc.titleData mining for municipal financial distress predictionen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroFacultad de Ciencias Económicas y Empresarialesen_US
dc.relation.eventtitle18th Industrial Conference on Data Mining ICDM 2018en_US
dc.relation.eventplaceNueva York. USAen_US
dc.relation.eventdate11-15 de Julioen_US


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