False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams

Summary: Introduces false-negative-oriented frequent-itemset mining for high-speed transactional streams, avoiding the exponential blowup caused by false positives under bounded memory. Chernoff-bound algorithms control missed frequent itemsets to guarantee recall, using less memory and CPU than prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: h726f5da98fae2397
Venue: VLDB
Year: 2004
Pagerank: 6.4174716e-05
Overall Rank: 4,778 | 67.88%
DOI: 10.1016/B978-012088469-8.50021-8

Incoming Non-self Citations Over Time

Authors

1. Jeffrey Xu Yu (Chinese University of Hong Kong)
2. Zhihong Chong (Fudan University)
3. Hongjun Lu (Hong Kong University of Science and Technology)
4. Aoying Zhou (Fudan University)

BibTeX Citation

@article{yu_vldb04,
        title = {{False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams}},
        author = {Yu, Jeffrey Xu and Chong, Zhihong and Lu, Hongjun and Zhou, Aoying},
        journal = {PVLDB},
        series = {{VLDB} '04},
        pages = {204--215},
        doi = {10.1016/B978-012088469-8.50021-8},
        url = {https://doi.org/10.1016/B978-012088469-8.50021-8},
        year = {2004}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,319	Augmented Sketch: Faster and More Accurate Stream Processing	2016	SIGMOD	0.00011045888
4,320	On Dense Pattern Mining in Graph Streams	2010	VLDB	6.6666852e-05
13,045	Using Association Rules for Fraud Detection in Web Advertising Networks	2005	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
29	Fast Algorithms for Mining Association Rules	1994	VLDB	0.0005121339
124	Approximate Frequency Counts over Data Streams	2002	VLDB	0.00030600691
858	What’s Hot and What’s Not: Tracking Most Frequent Items Dynamically	2003	PODS	0.000134266
870	Spectral Bloom Filters	2003	SIGMOD	0.0001334417

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5	9,390	Feasible Itemset Distributions in Data Mining: Theory and Application	2003	PODS
6	2,416	On Differentially Private Frequent Itemset Mining	2013	VLDB
7	14,308	Mining Frequent Itemsets with Bit Strings and Trie	2002	VLDB
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9	12,467	Resource-oriented Approximation for Frequent Itemset Mining from Bursty Data Streams	2014	SIGMOD
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