Squared Symmetric Formal Contexts and Their Connections with Correlation Matrices.

Formal Concept Analysis identifies hidden patterns in data that can be presented to the user or the data analyst. We propose a method for analyzing the correlation matrices based on Formal concept analysis. In particular, we define a notion of squared symmetric formal context and prove its properties. Transforming a correlation matrix into a squared symmetric formal context is feasible with the help of fuzzy logic. Thus, the concept hierarchies of squared symmetric formal contexts can be thoroughly investigated and visualized. Moreover, information hidden in such type of data can help to find some interrelations between attributes and can help to solve pending issues within enterprise or science. To illustrate our approach, we include our novel results by analyzing a correlation matrix with 36 variables computed from a real dataset.