Formal Concept Analysis
Convert Named Vector to Set
Convert Set to vector
Calculate Fuzzy Density
Calculate Concept Grades (Levels)
Calculate Concept Separation
Calculate Concept Stability
Compute Labels and Colors for Lattice Nodes
R6 class for a fuzzy concept with sparse internal representation
R6 class for a concept lattice
Concept Miners Registry
R6 class for a set of concepts
Equivalence Rules Registry
Export Layout to TikZ (LaTeX)
Set or get options for fcaR
fcaR: Tools for Formal Concept Analysis
Fetch a Formal Context from the FCA Repository
R6 class for a formal context
Get Metadata from the FCA Repository
Intersection (Logical AND) of Fuzzy Sets
Entailment between implication sets
Equality in Sets and Concepts
Difference in Sets
Implications that hold in a Formal Context
Partial Order in Sets and Concepts
Union (Logical OR) of Fuzzy Sets
Check if Set or FormalContext respects an ImplicationSet
Equivalence of sets of implications
R6 Class for Set of implications
Plot Concept Lattice
Parses a string into an implication
Parses several implications given as a string
Pipe operator
Print Details of Repository Contexts
Generate Random Formal Contexts
Generate a Random Distributive Context
Randomize an Existing Formal Context
Save TikZ Code to File
Scaling Registry
GUI to select and download a context from the repository
R6 class for a fuzzy set with sparse internal representation
Provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23> and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>. 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. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 <doi:10.1109/TFUZZ.2023.3330760>).
Useful links