FCAvizIR: Exploring Relational Data Set’s Implications Using Metrics and Topics

1. Alam, M., Le, T.N.N., Napoli, A.: Steps towards interactive formal concept analysis with latviz. In: Proceedings of the 5th International Workshop. “What can FCA do for Artificial Intelligence”? co-loc. with the Europ. Conference on Artificial Intelligence ECAI (2016)

   Google Scholar 

2. Baixeries, J., Codocedo, V., Kaytoue, M., Napoli, A.: Computing dependencies using FCA. In: Missaoui, R., Kwuida, L., Abdessalem, T. (eds.) Complex Data Analytics with Formal Concept Analysis, pp. 135–150. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-93278-7_6

   Chapter  Google Scholar 

3. Baldonado, M.Q.W., Woodruff, A., Kuchinsky, A.: Guidelines for using multiple views in information visualization. In: Proceedings of the working Conference on Advanced Visual Interfaces (AVI), pp. 110–119. ACM Press (2000)

   Google Scholar 

4. Bayardo Jr, R.J., Agrawal, R.: Mining the most interesting rules. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 145–154 (1999)

   Google Scholar 

5. Bazin, A.: On implication bases in n-lattices. Discret. Appl. Math. 273, 21–29 (2020)

   Article  MathSciNet  Google Scholar 

6. Bertet, K., Monjardet, B.: The multiple facets of the canonical direct unit implicational basis. Theor. Comput. Sci. 411(22–24), 2155–2166 (2010)

   Article  MathSciNet  Google Scholar 

7. Bostock, M., Ogievetsky, V., Heer, J.: \(D^{3}\) data-driven documents. IEEE Trans. Visual Comput. Graph. 17(12), 2301–2309 (2011)

   Article  Google Scholar 

8. Cordero, P., Enciso, M., Mora, Á., González, P.G.: An approach to visualize implications. In: ICFCA (Supplements), pp. 113–124 (2019)

   Google Scholar 

9. Cordero, P., Enciso, M., Mora, Á., Ojeda-Aciego, M.: Computing left-minimal direct basis of implications. In: Proceedings of the 10th International Conference on Concept Lattices and Their Applications. CEUR Workshop. Proceedings, vol. 1062, pp. 293–298 (2013)

   Google Scholar 

10. Ertek, G., Demiriz, A.: A framework for visualizing association mining results. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 593–602. Springer, Heidelberg (2006). https://doi.org/10.1007/11902140_63

    Chapter  Google Scholar 

11. FerrÃ, S.: Reconciling Expressivity and Usability in Information Access - From Filesystems to the Semantic Web. Habilitation thesis, Matisse, Univ. Rennes 1 (2014). habilitation à Diriger des Recherches (HDR), defended on November 6th

    Google Scholar 

12. Fister, I.: Information cartography in association rule mining. IEEE Trans. Emerg. Top. Comput. intell. 6(3), 660–676 (2021)

    Article  Google Scholar 

13. Fister, I., et al.: Visualization of numerical association rules by hill slopes. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds.) IDEAL 2020. LNCS, vol. 12489, pp. 101–111. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62362-3_10

    Chapter  Google Scholar 

14. Fister, I., Fister, I.: Association rules over time. Frontiers in nature-inspired industrial optimization, pp. 1–16 (2022)

    Google Scholar 

15. Fister Jr, I., Fister, I., Fister, D., Podgorelec, V., Salcedo-Sanz, S.: A comprehensive review of visualization methods for association rule mining: Taxonomy, challenges, open problems and future ideas. Exp. Syst. with App, p. 120901 (2023)

    Google Scholar 

16. Ganter, B., Wille, R.: Formal Concept Analysis. Springer Berlin Heidelberg, Berlin, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    Book  Google Scholar 

17. Guigues, J.L., Duquenne, V.: Familles minimales d’implications informatives résultant d’un tableau de données binaires. Mathématiques et sciences humaines 95, 5–18 (1986)

    Google Scholar 

18. Hahsler, M.: Arulesviz: interactive visualization of association rules with R. R J. 9(2), 163 (2017)

    Article  Google Scholar 

19. Hahsler, M., Karpienko, R.: Visualizing association rules in hierarchical groups. J. Bus. Econ. 87, 317–335 (2017)

    Google Scholar 

20. Hartigan, J.A., Kleiner, B.: A mosaic of television ratings. Am. Stat. 38(1), 32–35 (1984)

    Article  Google Scholar 

21. Hofmann, H.: Exploring categorical data: interactive mosaic plots. Metrika 51, 11–26 (2000)

    Article  Google Scholar 

22. Hrovat, G., Fister, I., Jr., Yermak, K., Stiglic, G., Fister, I.: Interestingness measure for mining sequential patterns in sports. J. Intell. Fuzzy Syst. 29(5), 1981–1994 (2015)

    Article  Google Scholar 

23. Mahrach, L., et al.: Combining implications and conceptual analysis to learn from a pesticidal plant knowledge base. In: Braun, T., Gehrke, M., Hanika, T., Hernandez, N. (eds.) Graph-Based Representation and Reasoning: 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20–22, 2021, Proceedings, pp. 57–72. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-86982-3_5

    Chapter  Google Scholar 

24. Munzner, T.: A nested process model for visualization design and validation. IEEE Trans. Visual Comput. Graphics 15(6), 921–928 (2009)

    Article  Google Scholar 

25. Neves, J.C.V., et al.: Exploring different paradigms to extract proper implications from high dimensional formal contexts. IEEE Access 8, 134161–134175 (2020)

    Article  Google Scholar 

26. Ong, K.H., Ong, K.L., Ng, W.K., Lim, E.P.: Crystalclear: active visualization of association rules. In: International Workshop on Active Mining (AM-2002). Citeseer (2002)

    Google Scholar 

27. Roberts, J.C.: State of the art: Coordinated & multiple views in exploratory visualization. In: Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV), pp. 61–71 (2007)

    Google Scholar 

28. Rouane-Hacène, M.R., Huchard, M., Napoli, A., Valtchev, P.: Relational concept analysis: mining concept lattices from multi-relational data. Ann. Math. Artif. Intell. 67(1), 81–108 (2013)

    Article  MathSciNet  Google Scholar 

29. Said, Z.B., Guillet, F., Richard, P., Picarougne, F., Blanchard, J.: Visualisation of association rules based on a molecular representation. In: 2013 17th International Conference on information visualisation, pp. 577–581. IEEE (2013)

    Google Scholar 

30. Sedlmair, M., Meyer, M.D., Munzner, T.: Design study methodology: Reflections from the trenches and the stacks. IEEE Trans. Visual Comput. Graphics 18(12), 2431–2440 (2012)

    Article  Google Scholar 

31. Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: Proc. IEEE Symp. on Visual Languages, pp. 336–343 (1996)

    Google Scholar 

32. Silvie, P., Martin, P., Huchard, M., Keip, P., Gutierrez, A., Sarter, S.: Prototyping a knowledge-based system to identify botanical extracts for plant health in sub-saharan africa. Plants 10, 896 (04 2021)

    Google Scholar 

33. Tsurinov, P., Shpynov, O., Lukashina, N., Likholetova, D., Artyomov, M.: Farm: hierarchical association rule mining and visualization method. In: Proceedings of 12th ACM Conference on bioinformatics, Comput. Biology, and Health Informatics, pp. 1 (2021)

    Google Scholar 

34. Unwin, A., Hofmann, H., Bernt, K.: The TwoKey plot for multiple association rules control. In: De Raedt, L., Siebes, A. (eds.) Principles of Data Mining and Knowledge Discovery, pp. 472–483. Springer Berlin Heidelberg, Berlin, Heidelberg (2001). https://doi.org/10.1007/3-540-44794-6_39

    Chapter  Google Scholar 

35. Wajnberg, M.: Analyse relationnelle de concepts : une méthode polyvalente pour l’extraction de connaissance. Thèse de doctorat, Université du Québec à Montréal, Université de Lorraine (Nov 2020). https://hal.science/tel-03042085

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