fcaR: fcaR: Tools for Formal Concept Analysis

Description Details References Examples


The aim of this package is to provide tools to perform fuzzy formal concept analysis (FCA) from within R. It provides functions to load and save a Formal Context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems.


The fcaR package provides data structures which allow the user to work seamlessly with formal contexts and sets of implications. More explicitly, three main classes are implemented, using the R6 object-oriented-programming paradigm in R:

Two additional helper classes are implemented:


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Ganter B, Wille R (1999). Formal concept analysis : mathematical foundations. Springer. ISBN 3540627715.

Cordero P, Enciso M, Mora Á, Pérez de Guzman I (2002). “SLFD Logic: Elimination of Data Redundancy in Knowledge Representation.” Advances in Artificial Intelligence - IBERAMIA 2002, 2527, 141-150. doi: 10.1007/3-540-36131-6_15 (URL: http://doi.org/10.1007/3-540-36131-6_15).

Belohlavek R (2002). “Algorithms for fuzzy concept lattices.” In Proc. Fourth Int. Conf. on Recent Advances in Soft Computing. Nottingham, United Kingdom, 200-205.

Hahsler M, Grun B, Hornik K (2005). “arules - a computational environment for mining association rules and frequent item sets.” J Stat Softw, 14, 1-25.

Mora A, Cordero P, Enciso M, Fortes I, Aguilera G (2012). “Closure via functional dependence simplification.” International Journal of Computer Mathematics, 89(4), 510-526. Belohlavek R, Cordero P, Enciso M, Mora Á, Vychodil V (2016). “Automated prover for attribute dependencies in data with grades.” International Journal of Approximate Reasoning, 70, 51-67.


# Build a formal context
fc_planets <- FormalContext$new(planets)

# Find its concepts and implications

# Print the extracted implications

neuroimaginador/fcaR documentation built on Dec. 9, 2020, 5:42 a.m.