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Mining Long Sequential Patterns in a Noisy Environment

Summary: Proposes a framework for noisy long-sequence mining via a compatibility matrix mapping observations to true symbols. Defines a match metric for real support and a border-collapse, sampling-based method to discover long patterns efficiently. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3429
Venue
SIGMOD
Year
2002
Pagerank
5.093636e-05
Overall Rank
12,838 | 11.92%
DOI
10.1145/564691.564738

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Authors

BibTeX Citation

@inproceedings{yang_sigmod02,
        title = {{Mining Long Sequential Patterns in a Noisy Environment}},
        author = {Yang, Jiong and Wang, Wei and Yu, Philip S. and Han, Jiawei},
        series = {{SIGMOD} '02},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/564691.564738},
        url = {https://dl.acm.org/doi/10.1145/564691.564738},
        year = {2002}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
6,305 A Regression-Based Temporal Pattern Mining Scheme for Data Streams 2003 VLDB 5.9204295e-05
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