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: 10400
Venue: VLDB
Year: 2012
Pagerank: 5.6560373e-05
Overall Rank: 7,543 | 47.58%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,495	Reducing Uncertainty of Schema Matching via Crowdsourcing	2013	VLDB	6.7265248e-05
8,695	Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks	2024	VLDB	5.4478131e-05
11,960	Beyond Itemsets: Mining Frequent Featuresets over Structured Items	2015	VLDB	5.1725247e-05

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	0.00066546358
162	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00028238267
226	Robust and Fast Similarity Search for Moving Object Trajectories	2005	SIGMOD	0.00024196346
319	On The Marriage of Lp-norms and Edit Distance	2004	VLDB	0.00021432452
6,030	Finding Frequent Items in Probabilistic Data	2008	SIGMOD	6.0614112e-05

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9,076	Feasible Itemset Distributions in Data Mining: Theory and Application	2003	PODS	5.3880466e-05
11,960	Beyond Itemsets: Mining Frequent Featuresets over Structured Items	2015	VLDB	5.1725247e-05
6,030	Finding Frequent Items in Probabilistic Data	2008	SIGMOD	6.0614112e-05