High Utility Sequential Rule

High-utility sequential rule mining (HUSRM) aims to identify valuable sequential patterns and their predictive relationships within datasets, going beyond simply finding frequent patterns to uncover rules with high utility and confidence. Recent research focuses on improving the efficiency and effectiveness of HUSRM algorithms, particularly by addressing challenges posed by interval-based events and incorporating correlation measures between rules and their constituent patterns. These advancements enhance the applicability of HUSRM in diverse fields like product recommendation and anomaly detection by providing more accurate and insightful predictions based on sequential data.