WSUI: an Efficient Weighted Sequential Pattern Mining Algorithm Using Indices and Frequency-Based Weighting

Sequential pattern mining aims to discover frequent ordered patterns in sequence databases, however, treating all items equally ignores their varying importance. Weighted sequential pattern mining (WSPM) addresses this limitation, yet existing algorithms incur high computational costs and severe memory overhead, especially at low support thresholds. We propose WSUI (Weighted Sequential pattern mining Using Index structures), a scalable algorithm that combines compact index structures with tight upper-bound pruning. WSUI introduces frequency-based automatic weighting to eliminate subjective weight assignment, extends the Data-IDList structure with sequence-level metadata for efficient evaluation, and employs a memory-efficient Pseudo-IDList to avoid materialization. Experiments show that WSUI outperforms the state-of-the-art EWSPM in terms of both runtime and memory usage.