Formal Concept Analysis (FCA) was introduced by Wille in the early eighties and in just a few decades it has established as a powerful tool for managing, representing and reasoning with information in object-attribute data tables. However, considering that an object does or does not have an attribute cannot be always modeled in absolutes. Thus, Fuzzy Formal Concept Analysis (FFCA) came to light in the early 2000s.
Even though this branch of mathematics has been proved to be accurate and give the maximum granularity of any particular problem modeled as a data table, the complexity of FCA algorithms makes it impractical to use in real-world applications, even worse is the case for FFCA. Therefore, the aim of this talk is to present recent approaches, both theoretical and empirical, that lower the time complexity of FFCA methods. In particular, we will explore how to take advantage of the mathematical structure of the model to avoid redundant computation.