fcaR | R Documentation |
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:
FormalContext
encapsulates the definition of a formal context (G, M, I)
, being G
the set of objects, M
the set of attributes and I
the (fuzzy) relationship matrix, and provides methods to operate on the context using FCA tools.
ImplicationSet
represents a set of implications over a specific formal context.
ConceptLattice
represents the set of concepts and their relationships, including methods to operate on the lattice.
Two additional helper classes are implemented:
Set
is a class solely used for visualization purposes, since it encapsulates in sparse format a (fuzzy) set.
Concept
encapsulates internally both extent and intent of a formal concept as Set
.
Since fcaR is an extension of the data model in the arules package, most of the methods and classes implemented interoperates with the main S4
classes in arules (transactions
and rules
).
Guigues J, Duquenne V (1986). “Familles minimales d'implications informatives résultant d'un tableau de données binaires.” Mathématiques et Sciences humaines, 95, 5-18.
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$new(planets)
# Find its concepts and implications
fc_planets$find_implications()
# Print the extracted implications
fc_planets$implications
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