Measuring the dependence of two features/variables in a dataset is a problem that finds applications in most sciences. It is generally either based on the probability theoretic definition of independence or done by evaluating how much a feature is a particular function of the other. In this paper, we introduce a definition of independence in formal concept analysis, a lattice theoretic framework, and we investigate whether it can be leveraged to measure the independence of numerical features. We exploit the connections between binary relations and algebraic and logical structures at the heart of formal concept analysis to propose three measures and we evaluate their potential using synthetic feature selection problems.