  Discovering meaningful conceptual structures is a substantial task in data
mining and knowledge discovery applications. While off-the-shelf
interestingness indices defined in Formal Concept Analysis may provide an
effective relevance evaluation in several situations, they frequently give
inadequate results when faced with massive formal contexts (and concept
lattices), and in the presence of irrelevant concepts. In this paper, we
introduce the Conceptual Relevance (CR) score, a new scalable interestingness
measurement for the identification of actionable concepts. From a conceptual
perspective, the minimal generators provide key information about their
associated concept intent. Furthermore, the relevant attributes of a concept
are those that maintain the satisfaction of its closure condition. Thus, the
guiding idea of CR exploits the fact that minimal generators and relevant
attributes can be efficiently used to assess concept relevance. As such, the CR
index quantifies both the amount of conceptually relevant attributes and the
number of the minimal generators per concept intent. Our experiments on
synthetic and real-world datasets show the efficiency of this measure over the
well-known stability index.
