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.

翻译：在知识发现应用中，从数据生成的模式集可能异常庞大，难以被分析人员探索。在形式概念分析框架下，已有研究通过稳定性指数及其他质量度量来识别重要形式概念。本文提出基础等价概念相关性得分（BECR），这是一种新颖的概念相关性兴趣度度量，旨在改进可操作概念的识别。从概念的角度来看，基础属性和等价属性被视为有意义的信息，并且对维持概念的概念结构至关重要。因此，BECR的基本思想是：概念内涵包含的基础属性、等价属性及最小生成子越多，其相关性越强。基于此，BECR对每个概念内涵中的这些属性及最小生成子进行量化。我们在合成数据集和真实世界数据集上的初步实验表明，与著名的稳定性指数相比，BECR具有更高的效率。