Detecting Important Patterns Using Conceptual Relevance Interestingness Measure
This work addresses the challenge of discovering meaningful conceptual structures in data mining for applications where existing measures fail with massive data or irrelevant concepts.
The paper tackles the problem of identifying actionable concepts in large formal contexts by introducing the Conceptual Relevance (CR) score, a scalable interestingness measure that outperforms the stability index in experiments on synthetic and real-world datasets.
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.