fcaR, Formal Concept Analysis with R

 

Abstract

Formal concept analysis (FCA) is a solid mathematical framework to manage information based on logic and lattice theory. It defines two explicit representations of the knowledge present in a dataset as concepts and implications. This paper describes an R package called fcaR that implements FCA’s core notions and techniques. Additionally, it implements the extension of FCA to fuzzy datasets and a simplification logic to develop automated reasoning tools. This package is the first to implement FCA techniques in R. Therefore, emphasis has been put on defining classes and methods that could be reusable and extensible by the community. Furthermore, the package incorporates an interface with the arules package, probably the most used package regarding association rules, closely related to FCA. Finally, we show an application of the use of the package to design a recommender system based on logic for diagnosis in neurological pathologies.

Funding

NoteProjects funding this work

Citation

Please, cite this work as:

[Cor+22] P. Cordero, M. Enciso, D. López-Rodríguez, et al. “fcaR, Formal Concept Analysis with R”. In: The R Journal 14 (1 2022). https://doi.org/10.32614/RJ-2022-014, pp. 341-361. ISSN: 2073-4859. DOI: 10.32614/RJ-2022-014.

NoteBibTeX

@article{RJ-2022-014,
     author = {Pablo Cordero and Manuel Enciso and Domingo {López-Rodríguez} and Ángel Mora},
     title = {fcaR, Formal Concept Analysis with R},
     journal = {The R Journal},
     year = {2022},
     note = {https://doi.org/10.32614/RJ-2022-014},
     doi = {10.32614/RJ-2022-014},
     volume = {14},
     issue = {1},
     issn = {2073-4859},
     pages = {341-361}
}

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fcaR, Formal Concept Analysis with R

Cites

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

1. Domingo López-Rodríguez, Manuel Ojeda-Hernández, Carlos Bejines (2025). New Simplification Rules for Databases with Positive and Negative Attributes. Mathematics DOI
2. Neha Gondal, Allison Wigen (2025). Professor-writers and machinist-painter-photographers: Investigating the duality between occupational categories and artistic hobbies. Poetics DOI
3. Domingo López-Rodríguez, Manuel Ojeda-Hernández, Tim Pattison (2025). Systems of implications obtained using the Carve decomposition of a formal context. Knowledge-Based Systems DOI
4. Imen Ben Sassi, Alexandre Bazin (2025). Closure Versus Dualization: A Comparison of Software Tools for Formal Concept Analysis. Lecture notes in computer science DOI
5. Gustavo Simas da Silva, Vânia Ribas Ulbricht (2025). PADRÕES DE SUSTENTABILIDADE: UMA ANÁLISE DE CONCEITOS FORMAIS DOS OBJETIVOS DE DESENVOLVIMENTO SUSTENTÁVEL NOS ESTADOS BRASILEIROS. Revista de Geopolítica DOI
6. Ľubomír Antoni, Peter Eliaš, Ján Guniš, et al. (2024). Bimorphisms and attribute implications in heterogeneous formal contexts. International Journal of Approximate Reasoning DOI
7. Manuel Ojeda-Hernández, Domingo López-Rodríguez, Ángel Mora (2024). A Formal Concept Analysis approach to hierarchical description of malware threats. Forensic Science International Digital Investigation DOI
8. Ján Guniš, Ľubomír Šnajder, Ľubomír Antoni, et al. (2024). Formal Concept Analysis of Students’ Solutions on Computational Thinking Game. IEEE Transactions on Education DOI
9. Francisco J. Valverde-Albacete, Carmen Peláez-Moreno (2024). A Formalization of Multilabel Classification in Terms of Lattice Theory and Information Theory: Concerning Datasets. Mathematics DOI
10. Tom Hanika, Robert Jäschke (2024). A Repository for Formal Contexts. Lecture notes in computer science DOI
11. Sadriddinov Ilkhomjon Rovshan Ugli, Sony Peng, Sophort Siet, et al. (2024). Algorithm for Mining Maximal Balanced Bicliques Using Formal Concept Analysis. IEEE Access DOI
12. Domingo López-Rodríguez, Manuel Ojeda-Hernández (2024). Rearrangement of Fuzzy Formal Contexts for Reducing Cost of Algorithms. Lecture notes in computer science DOI
13. Manuel Ojeda-Hernández, Domingo López-Rodríguez (2024). Enhancing Performance of FCA Algorithms via Rearrangement of Formal Contexts. DOI
14. Chongkolnee Rungruang, Pakwan Riyapan, Arthit Intarasit, et al. (2023). RFM model customer segmentation based on hierarchical approach using FCA. Expert Systems with Applications DOI
15. Manuel Ojeda-Hernández, Domingo López-Rodríguez, Ángel Mora (2023). Lexicon-based sentiment analysis in texts using Formal Concept Analysis. International Journal of Approximate Reasoning DOI
16. Pavol Sokol, Ľubomír Antoni, Ondrej Krídlo, et al. (2023). Formal concept analysis approach to understand digital evidence relationships. International Journal of Approximate Reasoning DOI
17. Lingjuan Yao, Shengwen Wang, Qingguo Li, et al. (2023). Continuous lattices in formal concept analysis. Soft Computing DOI
18. Ondrej Krídlo, Domingo López-Rodríguez, Ľubomír Antoni, et al. (2023). Connecting concept lattices with bonds induced by external information. Information Sciences DOI