Parent Selection Criterion for Extracting Trees from Concept Lattices

Traditional software in Formal Concept Analysis makes little use of visualization techniques, producing poorly readable concept lattice representations when the number of concepts exceeds a few dozens. This is problematic as the number of concepts in such lattices grows significantly with the size of the data and the number of its dimensions. In this work we propose several methods to enhance the readability of concept lattices firstly though colouring and distortion techniques, and secondly by extracting and visualizing trees derived from concept lattice structures. These contributions represent an important step in the visual analysis of conceptual structures, as domain experts may visually explore larger datasets that traditional visualizations of concept lattice cannot represent effectively.