A novel lift adjustment methodology for improving association rule interpretation

Association rules can offer a human-interpretable insight extracted from data. The lift measures used for evaluating association rules in classical Association Rule Mining (ARM) contexts are mainly based on traditional and well-known ones but suffer from interpretation inadequacy when dealing with skewed distributions or low support. This study introduces a new lift adjustment approach with four methods to overcome traditional lift measures and identify the best rules in association rule mining. More concretely, our main objective is to improve the interpretability of association rules to make them more practically relevant for decision-making. We propose an approach incorporating four novel lift adjustment methods (smoothed, weighted, log, and threshold-adjusted lift) to achieve this. We introduce a flexible, dynamic approach combined with four new lift adjustment methods: smoothed, weighted, logarithm, and threshold-adjusted lift. Each technique addresses specific limitations of the traditional lift measure and better captures the reliable representation of item associations by exaggerating stronger relationships or smoothing weaker ones. The proposed methods applied context-aware rule evaluation and adjustment based on measures of relative significance (e.g., Jaccard similarity). The experimental results involving real-world data and synthetic datasets reveal new methods' effectiveness and robustness in understanding the strengths of association rules and provide a comprehensive view that considers item importance. We evaluate the performance stability of our proposed methods using statistical analysis, including ANOVA, chi-squared, t-tests, and effect size metrics.