Boosting the Learning for Ranking Patterns

TL;DR

This paper introduces a fast, interactive method for learning user-specific pattern rankings by aggregating multiple interestingness measures through a weighted linear model, significantly improving efficiency and robustness.

Contribution

It formulates pattern ranking as a multicriteria decision problem and applies an AHP-based interactive learning approach with a sensitivity heuristic for active mode, reducing computation time.

Findings

Reduces running time compared to existing methods

Achieves high-quality, user-aligned pattern rankings

Demonstrates robustness to user errors

Abstract

Discovering relevant patterns for a particular user remains a challenging tasks in data mining. Several approaches have been proposed to learn user-specific pattern ranking functions. These approaches generalize well, but at the expense of the running time. On the other hand, several measures are often used to evaluate the interestingness of patterns, with the hope to reveal a ranking that is as close as possible to the user-specific ranking. In this paper, we formulate the problem of learning pattern ranking functions as a multicriteria decision making problem. Our approach aggregates different interestingness measures into a single weighted linear ranking function, using an interactive learning procedure that operates in either passive or active modes. A fast learning step is used for eliciting the weights of all the measures by mean of pairwise comparisons. This approach is based on Analytic Hierarchy Process (AHP), and a set of user-ranked patterns to build a preference matrix, which compares the importance of measures according to the user-specific interestingness. A sensitivity based heuristic is proposed for the active learning mode, in order to insure high quality results with few user ranking queries. Experiments conducted on well-known datasets show that our approach significantly reduces the running time and returns precise pattern ranking, while being robust to user-error compared with state-of-the-art approaches.