Sequential Pattern

Sequential pattern mining focuses on identifying recurring ordered sequences within datasets, aiming to uncover meaningful relationships and predict future events. Current research emphasizes efficient algorithms for discovering statistically significant patterns, particularly in high-dimensional or uncertain data, often employing techniques like neural networks (e.g., GNNs) and resampling methods to improve accuracy and interpretability. This field is crucial for applications ranging from recommender systems and anomaly detection in industrial settings to analyzing clinical data and understanding complex biological processes, offering valuable insights across diverse domains.