Maximal Ordinal Two-Factorizations
This work addresses a specific computational challenge in formal concept analysis for data visualization, with incremental contributions to algorithm design.
The paper tackles the problem of finding maximal ordinal two-factorizations in formal contexts for data visualization, showing that the decision problem is NP-complete and providing an algorithm to compute large factorizations.
Given a formal context, an ordinal factor is a subset of its incidence relation that forms a chain in the concept lattice, i.e., a part of the dataset that corresponds to a linear order. To visualize the data in a formal context, Ganter and Glodeanu proposed a biplot based on two ordinal factors. For the biplot to be useful, it is important that these factors comprise as much data points as possible, i.e., that they cover a large part of the incidence relation. In this work, we investigate such ordinal two-factorizations. First, we investigate for formal contexts that omit ordinal two-factorizations the disjointness of the two factors. Then, we show that deciding on the existence of two-factorizations of a given size is an NP-complete problem which makes computing maximal factorizations computationally expensive. Finally, we provide the algorithm Ord2Factor that allows us to compute large ordinal two-factorizations.