Fast Concept Analysis

Formal concept analysis is increasingly used for large contexts that are built by programs. This paper presents an efficient algorithm for concept analysis that computes concepts together with their explicit lattice structure. An experimental evaluation uses randomly generated contexts to compare the running time of the presented algorithm with two other algorithms. Running time increases quadratically with the number of concepts, but with a small quadratic component. At least contexts with sparsely filled context tables cause concept lattices grow quadratically with respect to the size of their base relation. The growth rate is controlled by the density of context tables. Modest growth combined with efficient algorithms lead to fast concept analysis.