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Mining Frequent Itemsets over Uncertain Databases

Summary: Uncertain databases: itemset support is a random variable; two frequent-itemset definitions (expected vs probabilistic). The paper shows a tight connection and unification for large data, and provides eight algorithms with fair cross-definition comparisons. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10587
Venue
VLDB
Year
2012
Pagerank
5.5739357e-05
Overall Rank
7,657 | 47.47%
DOI
10.14778/2350229.2350277

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{tong_vldb12,
        title = {{Mining Frequent Itemsets over Uncertain Databases}},
        author = {Tong, Yongxin and Chen, Lei and Cheng, Yurong and Yu, Philip S.},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {11},
        pages = {1650--1661},
        doi = {10.14778/2350229.2350277},
        url = {https://doi.org/10.14778/2350229.2350277},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank Cited Paper Year Venue Pagerank
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0006567919
161 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00027981772
221 Robust and Fast Similarity Search for Moving Object Trajectories 2005 SIGMOD 0.00024224879
303 On The Marriage of Lp-norms and Edit Distance 2004 VLDB 0.00021956234
6,120 Finding Frequent Items in Probabilistic Data 2008 SIGMOD 5.9693818e-05
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