  In knowledge discovery applications, the pattern set generated from data can
be tremendously large and hard to explore by analysts. In the Formal Concept
Analysis (FCA) framework, there have been studies to identify important formal
concepts through the stability index and other quality measures. In this paper,
we introduce the Base-Equivalent Conceptual Relevance (BECR) score, a novel
conceptual relevance interestingness measure for improving the identification
of actionable concepts. From a conceptual perspective, the base and equivalent
attributes are considered meaningful information and are highly essential to
maintain the conceptual structure of concepts. Thus, the basic idea of BECR is
that the more base and equivalent attributes and minimal generators a concept
intent has, the more relevant it is. As such, BECR quantifies these attributes
and minimal generators per concept intent. Our preliminary experiments on
synthetic and real-world datasets show the efficiency of BECR compared to the
well-known stability index.
