Manipulating Triadic Concept Analysis Contexts Through Binary Decision Diagrams

Formal Concept Analysis (FCA) is an approach based on the mathematization and hierarchy of formal concepts. Nowadays, with the increasing of social network for personal and professional usage, more and more applications of data analysis on environments with high dimensionality (Big Data) have been discussed in the literature. Through the Formal Concept Analysis and Triadic Concept Analysis, it is possible to extract database knowledge in a hierarchical and systematized representation. It is common that the data set transforms the extraction of this knowledge into a problem of high computational cost. Therefore, this paper has an objective to evaluate the behavior of the algorithm for extraction triadic concepts using TRIAS in high dimensional contexts. It was used a synthetic generator known as SCGaz (Synthetic Context Generator a-z). After the analysis, it was proposed a representation of triadic contexts using a structure known as Binary Decision Diagram (BDD).