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Applying binary decision diagram to extract concepts from triadic formal context

Triadic Concept Analysis (TCA) is an applied mathematical technique for data analysis which the relations between objects, attributes and conditions are identified. However, the volume of information to be processed could make TCA impracticable. For example, with the increasing of social network for personal (Facebook) and professional (LinkedIn) usage, more and more applications of data analysis on environments with high dimensionality (Big Data) have been discussed in the literature. This paper has as an objective to evaluate the behavior of the TRIAS algorithm in order to extract triadic concepts in high dimensional contexts. The experiments shows that our approach has a better performance - up to 33% faster - than its original algorithm.