Analytic hierarchy process improves pattern ranking with fewer user queries
Boosting the Learning for Ranking Patterns
Artificial Intelligence
Summary
Finding interesting patterns that match a user's personal needs is hard because many ways to measure patterns exist but none work well for everyone. The authors propose using a method called Analytic Hierarchy Process to combine different pattern scores based on what the user prefers. They add a smart way to ask the user fewer questions but still learn what they like. Their tests show this method is faster and more accurate than previous ones, even if the user makes some mistakes.
What this means in practice
- •For data analytics teams: Produce personalized ranked pattern lists faster by reducing user input while maintaining ranking accuracy in exploratory analysis.
- •For market analysts: Tailor pattern discovery processes to specific customer preferences quickly, enabling more targeted market segment insights.
Authors
Nassim Belmecheri, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, Samir Loudni
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.