Boosting the Learning for Ranking Patterns

Abstract:Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness mea- sures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multicriteria decision making problem. Our approach uses Analytic Hierarchy Process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user error, compared to state-of-the-art approaches.

Artificial Intelligence (cs.AI)

arXiv:2610.16511 [cs.AI]

(or arXiv:2610.16511v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.16511 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.3390/a16050218 Focus to learn more DOI(s) linking to related resources

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