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Efficient sliding-window-based algorithms for mining frequent weighted utility closed patterns over dynamic quantitative data streams
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DOI：10.1080/24751839.2026.2673637.png)
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Abstract
En 中文
Mining frequent weighted utility closed patterns (FWUCPs) from dynamic quantitative data streams is a challenging yet practically important task. The task is to discover meaningful patterns in real-time streams with dynamically changing item weights. To address this challenge, we formally define the problem of FWUCP mining under a sliding window model with dynamically changing weights. We propose two novel single-pass algorithms: CTC-MINER, based on a cyclic tidset (CTset) structure, and SAC-MINER, which exploits a stream-adaptive tree (SA-Tree) with compressed SAN-List. Both algorithms are designed to efficiently handle continuous updates while ensuring correct closure checking in a streaming setting. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed framework. The results show that SAC-MINER achieves significant advantages in runtime and scalability on dense and moderately sparse datasets, while CTC-MINER remains competitive on extremely sparse or low-cardinality datasets. These findings highlight the trade-offs between compression-based and tidset-based strategies and confirm the robustness of our framework for real-world, large-scale data stream mining.
Keywords:
Dynamic quantitative data streams
frequent weighted utility closed patterns
N-list structure
sliding window
Journal
IF:
1.7
Papers:
68
Citations:
419
