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English Information

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Authors
# Name
1 João Pedro Pereira(santosjp167@gmail.com)
2 Luis Enrique Zarate(zarate@pucminas.br)
3 Mark Song(song@pucminas.br)

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Reference
# Reference
1 Biedermann, K. (1997). How triadic diagrams represent conceptual structures. In Lukose, D., Delugach, H., Keeler, M., Searle, L., and Sowa, J., editors, Conceptual Structures: Fulfilling Peirce’s Dream, pages 304–317, Berlin, Heidelberg. Springer Berlin Heidelberg.
2 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.
3 Diggle, P. J. (1994). Analysis of longitudinal data. Technometrics, 45:181 – 181.
4 Ganter, B. and Wille, R. (2012). Formal concept analysis: mathematical foundations. Springer Science & Business Media.
5 Gupta, A., Kumar, N., and Bhatnagar, V. (2007). Analysis of Medical Data using Data Mining and Formal Concept Analysis
6 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.
7 Lehmann, F. and Wille., R. (1995). A triadic approach to formal concept analysis. conceptual structures: ap-plications, implementation and theory. Springer.
8 Missaoui, R. and Emamirad, K. (2017). Lattice miner-a formal concept analysis tool. In 14th International Conference on Formal Concept Analysis, page 91.
9 Missaoui, R. and Kwuida., L. (2011). Mining triadic associa-tion rules from ternary relations. In Inter. Conf. on Formal Concept, Springer, pages 204–218.
10 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.
11 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.
12 Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., and Lakhal, L. (2002). Computing iceberg concept lattices with titanic. Data Knowledge Engineering, page 189–222.
13 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.