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OSP-Miner: Mining one-off weak-gap strong sequential patterns
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AI
DOI：10.1016/j.ins.2025.122871.png)
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Abstract
En 中文
In sequential pattern mining (SPM), sequences can be divided into two categories: simple sequences composed of items and general sequences composed of itemsets. Previous one-off SPM methods have mainly discovered patterns from simple sequences. More importantly, although most SPM methods discover all frequent patterns, some of the patterns mined in this way are not of interest to users. To tackle these issues, all items are divided into strong or weak interest, based on the interest level of the user, and we discover one-off weak-gap strong patterns (OSPs) composed of strong interest items from sequences with itemsets, using an effective algorithm called OSP-Miner. At the preparation stage, OSP-Miner creates binomial search lists for patterns of length two, thereby avoiding the redundant subpattern matching processes. At the support calculation stage, OSP-Miner first creates m-1 (m > 2) level nodes based on the binomial search lists, and then employs a depth-first search strategy to calculate the support of the candidate pattern. At the candidate pattern generation stage, I-Join and S-Join algorithms are employed to reduce the number of candidate patterns. Experimental results show that OSP-Miner outperforms competitive algorithms, and a case study demonstrates that OSP-Miner yields better performance in a clustering analysis of driving trajectories.
Keywords:
Sequential pattern mining
Weak-gap constraint
Frequent pattern
One-off condition
Journal
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