Sequential Pattern Mining

Sequential pattern mining (SPM) aims to discover statistically significant patterns in ordered data sequences, such as customer purchase histories or student learning behaviors. Current research focuses on improving the efficiency and scalability of SPM algorithms, particularly for high-utility pattern mining and targeted pattern discovery, employing techniques like compact data structures (e.g., hybrid tries) and novel pruning strategies to reduce computational complexity. These advancements are crucial for analyzing large datasets in various fields, enabling more effective applications in areas such as education, recommendation systems, and crime prediction, where understanding temporal relationships is vital.