Conceptual Relevance Index for Identifying Actionable Formal Concepts

Abstract

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 ($\mathcal {CR}$) score, a new scalable interestingness measure for the identification of relevant concepts. From a conceptual perspective, 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, $\mathcal {CR}$ exploits the fact that minimal generators and relevant attributes can be efficiently used to assess concept relevance. As such, the $\mathcal {CR}$ 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.