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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
10399
Venue
VLDB
Year
2012
Pagerank
4.6914549e-05
Overall Rank
7,633 | 46.90%
DOI
-

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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
13 Mining Association Rules between Sets of Items in Large Databases 1993 SIGMOD 0.0010864752
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
251 Robust and Fast Similarity Search for Moving Object Trajectories 2005 SIGMOD 0.00030644658
358 On The Marriage of Lp-norms and Edit Distance 2004 VLDB 0.0002599481
5,796 Finding Frequent Items in Probabilistic Data 2008 SIGMOD 5.3240234e-05
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