Bisection-based merging algorithm for creation of one-sided concept lattices

In this paper we provide the new version of algorithm for creation model called Generalized One-Sided Concept Lattice (GOSCL). This model provides the specific fuzzy version of data analytical method based on the approach known as Formal Concept Analysis (FCA), which supports data tables containing the multiple types of attributes defined as fuzzy sets. The acquisition of the FCA models is computationally complex task and it is important to find more effective algorithms for their creation. Therefore, we have designed the algorithm for the reduction of the computation times, which is based on the simple division of input data table using bisection-based approach and then merging procedure compose the local models into one finally merged concept lattice for the complete input data. We present the illustrative experiments which prove the applicability of the presented algorithm for sparse data inputs, where it is possible to get significant decrease of computation times. More effective algorithm for sparse data can be useful for the application of FCA-based models in sparse domains like information retrieval or text analysis.