Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline (2020)
- Authors:
- USP affiliated authors: BATISTA, GUSTAVO ENRIQUE DE ALMEIDA PRADO ALVES - ICMC ; JACINTHO, LUCAS HENRIQUE MANTOVANI - ICMC ; SILVA, TIAGO PINHO DA - ICMC ; PARMEZAN, ANTONIO RAFAEL SABINO - ICMC
- Unidade: ICMC
- DOI: 10.5753/kdmile.2020.11979
- Subjects: MINERAÇÃO DE DADOS; APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE PADRÕES; ANÁLISE DE SÉRIES TEMPORAIS; ELEIÇÕES DIRETAS
- Keywords: preferential voting; spatio-temporal patterns; voting behavior
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: SBC
- Publisher place: Porto Alegre
- Date published: 2020
- Source:
- Título: Proceedings
- Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
JACINTHO, Lucas Henrique Mantovani et al. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11979. Acesso em: 02 abr. 2026. -
APA
Jacintho, L. H. M., Silva, T. P. da, Parmezan, A. R. S., & Batista, G. E. de A. P. A. (2020). Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11979 -
NLM
Jacintho LHM, Silva TP da, Parmezan ARS, Batista GE de APA. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline [Internet]. Proceedings. 2020 ;[citado 2026 abr. 02 ] Available from: https://doi.org/10.5753/kdmile.2020.11979 -
Vancouver
Jacintho LHM, Silva TP da, Parmezan ARS, Batista GE de APA. Brazilian presidential elections: analysing voting patterns in time and space using a simple data science pipeline [Internet]. Proceedings. 2020 ;[citado 2026 abr. 02 ] Available from: https://doi.org/10.5753/kdmile.2020.11979 - Geographic context-based stacking learning for election prediction from socio-economic data
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