Enhancing Actionable Formal Concept Identification with Base-Equivalent Conceptual-Relevance
This work addresses the challenge for analysts in knowledge discovery applications, such as Formal Concept Analysis, by providing an incremental improvement in identifying actionable concepts.
The paper tackles the problem of identifying important formal concepts in large pattern sets from data by introducing the Base-Equivalent Conceptual Relevance (BECR) score, a novel interestingness measure that quantifies base and equivalent attributes and minimal generators per concept intent, with preliminary experiments showing its efficiency compared to the stability index.
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.