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dc.contributor.authorRosales-Salas, Jorge [Univ Mayor, Fac Humanidades, Ctr Invest Sociedad Tecnol & Futuro Humano, Av Portugal 351, Santiago, Chile]es_CL
dc.contributor.authorMaldonado, Sebastiánes_CL
dc.contributor.authorSeret, Alexes_CL
dc.date.accessioned2020-04-08T14:11:55Z
dc.date.accessioned2020-04-13T18:12:56Z
dc.date.available2020-04-08T14:11:55Z
dc.date.available2020-04-13T18:12:56Z
dc.date.issued2018es_CL
dc.identifier.citationRosales-Salas, J., Maldonado, S., & Seret, A. (2018). Understanding time use via data mining: A clustering-based framework. Intelligent Data Analysis, 22(3), 597-616.es_CL
dc.identifier.issn1088-467Xes_CL
dc.identifier.issn1571-4128es_CL
dc.identifier.urihttps://doi.org/10.3233/IDA-173708es_CL
dc.identifier.urihttp://repositorio.umayor.cl/xmlui/handle/sibum/6318
dc.description.abstractIn this work, a data mining framework is proposed to improve the understanding of how people allocate their time. Using a multivariate approach, we performed a clustering procedure, and subsequently a regression analysis to detect which variables influence individual time use for each cluster found. Results suggest that the impact of various sociodemographic variables on sleep and work depends significantly on the characteristics of the individuals analyzed. This suggests that inquiries into time allocation and individual behavior should no longer be limited to discussions focused only on single variables. Based on our results, we recommend that researchers advance their methodological analysis towards a multifactorial approach and include clustering as a fundamental step. Proper identification of the most significant variables involved in time allocation decisions would allow researchers to better analyze and interpret their data and results.es_CL
dc.language.isoenes_CL
dc.publisherIOS PRESSes_CL
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourceIntell. Data Anal., 2018. 22(3): p. 597-616
dc.subjectComputer Science, Artificial Intelligencees_CL
dc.titleUnderstanding time use via data mining: A clustering-based frameworkes_CL
dc.typeArtículoes_CL
umayor.facultadCIENCIASes_CL
umayor.politicas.sherpa/romeoSIN INFORMACIÓNes_CL
umayor.indexadoWOS:000432011700009es_CL
umayor.indexadoSIN PMIDes_CL
dc.identifier.doiDOI: 10.3233/IDA-173708es_CL]
umayor.indicadores.wos-(cuartil)Q4es_CL
umayor.indicadores.scopus-(scimago-sjr)SCIMAGO/ INDICE H: 43 Hes_CL


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