Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams

Summary: Resource-oriented approximation for frequent itemset mining on bursty data streams; memory bounded to O(k) and per-transaction time O(kL), avoiding exponential blowup. Output error is bounded with possible false negatives only under certain conditions; dynamic stream reduction and experiments show it outperforms prior space-limited FIM-DS methods. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 4966
Venue: SIGMOD
Year: 2014
Pagerank: 5.093636e-05
Overall Rank: 12,176 | 16.47%
DOI: 10.1145/2588555.2612171

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Authors

1. Yoshitaka Yamamoto (University of Yamanashi)
2. Koji Iwanuma (University of Yamanashi)
3. Shoshi Fukuda (University of Yamanashi)

BibTeX Citation

@inproceedings{yamamoto_sigmod14,
        title = {{Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams}},
        author = {Yamamoto, Yoshitaka and Iwanuma, Koji and Fukuda, Shoshi},
        series = {{SIGMOD} '14},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2588555.2612171},
        url = {https://dl.acm.org/doi/10.1145/2588555.2612171},
        year = {2014}
}

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Rank	Citing Paper	Year	Venue	Pagerank

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
122	Approximate Frequency Counts over Data Streams	2002	VLDB	0.00031260115

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