Explorando Bases de Dados Longitudinais via Análise Triádica de Conceito - Um Estudo de Caso na Percepção da COVID-19
Resumo
Bases de dados longitudinais são comumente encontradas na área da saúde. Essas bases registram observações de uma mesma amostra de indivíduos durante períodos de tempo consecutivos denominados ondas. Neste trabalho propomos aplicar a Análise Triádica de Conceito para obter regras triádicas, que correspondem a regras de associação com condições, para descrever as relações temporais existentes entre as ondas do estudo. Os resultados mostram uma estratégia promissora para descrever bases de dados deste tipo. Como estudo de caso, foi utilizado uma base de dados a respeito das atitudes e percepções de indivíduos de uma população durante o enfrentamento da pandemia do COVID-19.
Palavras-chave:
Descoberta de Conhecimento, Bases de dados longitudinais, Análise Formal de Conceito, Análise Triádica
Referências
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De Coninck, D., d’Haenens, L., Molenberghs, G., Declercq, A., Delecluse, C., Van Roie, E., and Matthijs, K. (2022). Updating ‘perceptions and opinions on the covid-19 pandemic in flanders, belgium’ with data of two additional waves of a longitudinal study. Data in Brief, 42:108010.
Diggle, P. J. (1994). Analysis of longitudinal data. Technometrics, 45:181 – 181.
Ganter, B. and Wille, R. (2012). Formal concept analysis: mathematical foundations. Springer Science & Business Media.
Gupta, A., Kumar, N., and Bhatnagar, V. (2007). Analysis of Medical Data using Data Mining and Formal Concept Analysis.
Kim, E.-H., Kim, H.-G., Hwang, S.-H., and Lee, S.-I. (2015). Farm: An fca-based association rule miner. Knowledge-Based Systems, 85:277–297.
Lehmann, F. and Wille., R. (1995). A triadic approach to formal concept analysis. conceptual structures: ap-plications, implementation and theory. Springer.
Missaoui, R. and Emamirad, K. (2017). Lattice miner-a formal concept analysis tool. In 14th International Conference on Formal Concept Analysis, page 91.
Missaoui, R. and Kwuida., L. (2011). Mining triadic associa-tion rules from ternary relations. In Inter. Conf. on Formal Concept, Springer, pages 204–218.
Ribeiro, C. E., Brito, L. H. S., Nobre, C. N., Freitas, A. A., and Zárate, L. E. (2017). A revision and analysis of the comprehensiveness of the main longitudinal studies of human aging for data mining research. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3):e1202.
Singh, P. K., Kumar, C. A., and Gani, A. (2016). A comprehensive survey on formal concept analysis, its research trends and applications. 26(2):495–516.
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., and Lakhal, L. (2002). Computing iceberg concept lattices with titanic. Data Knowledge Engineering, page 189–222.
Wille, R. (2001). Why can concept lattices support knowledge discovery in databases? Proceedings of the concept lattices based knowledge discovery in databases workshop, pages 7–20.
De Coninck, D., d’Haenens, L., Molenberghs, G., Declercq, A., Delecluse, C., Van Roie, E., and Matthijs, K. (2022). Updating ‘perceptions and opinions on the covid-19 pandemic in flanders, belgium’ with data of two additional waves of a longitudinal study. Data in Brief, 42:108010.
Diggle, P. J. (1994). Analysis of longitudinal data. Technometrics, 45:181 – 181.
Ganter, B. and Wille, R. (2012). Formal concept analysis: mathematical foundations. Springer Science & Business Media.
Gupta, A., Kumar, N., and Bhatnagar, V. (2007). Analysis of Medical Data using Data Mining and Formal Concept Analysis.
Kim, E.-H., Kim, H.-G., Hwang, S.-H., and Lee, S.-I. (2015). Farm: An fca-based association rule miner. Knowledge-Based Systems, 85:277–297.
Lehmann, F. and Wille., R. (1995). A triadic approach to formal concept analysis. conceptual structures: ap-plications, implementation and theory. Springer.
Missaoui, R. and Emamirad, K. (2017). Lattice miner-a formal concept analysis tool. In 14th International Conference on Formal Concept Analysis, page 91.
Missaoui, R. and Kwuida., L. (2011). Mining triadic associa-tion rules from ternary relations. In Inter. Conf. on Formal Concept, Springer, pages 204–218.
Ribeiro, C. E., Brito, L. H. S., Nobre, C. N., Freitas, A. A., and Zárate, L. E. (2017). A revision and analysis of the comprehensiveness of the main longitudinal studies of human aging for data mining research. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(3):e1202.
Singh, P. K., Kumar, C. A., and Gani, A. (2016). A comprehensive survey on formal concept analysis, its research trends and applications. 26(2):495–516.
Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., and Lakhal, L. (2002). Computing iceberg concept lattices with titanic. Data Knowledge Engineering, page 189–222.
Wille, R. (2001). Why can concept lattices support knowledge discovery in databases? Proceedings of the concept lattices based knowledge discovery in databases workshop, pages 7–20.
Publicado
29/09/2025
Como Citar
SANTOS, João P.; SONG, Mark A. J.; ZÁRATE, Luis E..
Explorando Bases de Dados Longitudinais via Análise Triádica de Conceito - Um Estudo de Caso na Percepção da COVID-19. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 19. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 33-40.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2025.248086.
