Sequential Rule Mining

Sequential rule mining (SRM) aims to discover meaningful relationships between ordered sequences of events, enabling prediction and recommendation in diverse applications. Current research emphasizes improving the efficiency and effectiveness of SRM algorithms, focusing on techniques like utility-driven mining, correlation analysis between rules, and targeted rule discovery to reduce computational costs and enhance the relevance of mined rules. These advancements are crucial for handling the ever-increasing volume and complexity of sequential data, leading to more accurate and insightful pattern discovery in fields ranging from e-commerce to weather forecasting.