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

Author:

Musslin Lola,Bazin Alexandre,Huchard Marianne,Martin Pierre,Poncelet Pascal,Raveneau Vincent,Sallaberry Arnaud

Publisher

Springer Nature Switzerland

Reference35 articles.

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)

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

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)

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)

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

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