TaNSP: An efficient target pattern mining algorithm based on negative sequential pattern

doi:10.1016/j.ipm.2026.104643

TaNSP: An efficient target pattern mining algorithm based on negative sequential pattern

2026-01-22

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DOI：10.1016/j.ipm.2026.104643

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Target pattern mining (TPM) aims to return sets of target patterns related to a user-queried target sequence. However, existing TPM research is confined to positive sequential patterns, overlooking negative sequential patterns, which results in limited decision support capabilities. Moreover, introducing negative sequential patterns faces challenges of low mining efficiency and limited pruning techniques. To address these issues, we propose an efficient target pattern mining algorithm based on negative sequential pattern, called TaNSP, to achieve TPM for negative sequence as the user-queried target sequence and output negative sequential patterns containing the target query sequence, while also supporting positive sequential patterns. Specifically, we propose a pruning strategy based on a triple bitmap to guide pattern generation and improve mining efficiency. Then, we propose a pruning strategy to address the limitations of pruning techniques when the negative sequence is the target query sequence. The experimental results on six datasets demonstrate that, compared to the baseline method, TaNSP can increase operational efficiency by more than twice, demonstrating excellent scalability and practicality.

TaNSP: An efficient target pattern mining algorithm based on negative sequential pattern

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