Detecting Important Patterns Using Conceptual Relevance Interestingness Measure

TL;DR

This paper introduces the Conceptual Relevance (CR) score, a scalable and effective interestingness measure for identifying meaningful concepts in large formal contexts, outperforming existing indices like stability.

Contribution

The paper proposes the CR score, leveraging minimal generators and relevant attributes to improve concept relevance assessment in large data sets.

Findings

CR outperforms stability index in experiments

CR efficiently identifies actionable concepts in large datasets

The method is validated on synthetic and real-world data

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 (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.