Treatment of L-Fuzzy contexts with absent values

This work shows how to extract the missing information from an interval-valued L-Fuzzy context with some unknown values. Absent values are replaced using implications between attributes with high levels of support and confidence. Three kinds of implications are defined and analyzed for this purpose. We apply these results to an electrical network simulation, where the estimated relations between faulty power lines and voltage measurements can be compared with their real values.