Controlling False Positives in Association Rule Mining

Summary: Frames association-rule mining as a multiple-testing problem, showing that uncorrected enumeration yields many chance discoveries. Evaluates direct, permutation-based, and holdout corrections: permutation offers highest power but is costly, motivating techniques to reduce its computation. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 10570
Venue: VLDB
Year: 2012
Pagerank: 5.093636e-05
Overall Rank: 12,328 | 15.42%
DOI: 10.14778/2078324.2078325

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Guimei Liu (National University of Singapore)
2. Haojun Zhang (National University of Singapore)
3. Limsoon Wong (National University of Singapore)

BibTeX Citation

@article{liu_vldb12,
        title = {{Controlling False Positives in Association Rule Mining}},
        author = {Liu, Guimei and Zhang, Haojun and Wong, Limsoon},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {2},
        pages = {145--156},
        doi = {10.14778/2078324.2078325},
        url = {https://doi.org/10.14778/2078324.2078325},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 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
711	Beyond Market Baskets: Generalizing Association Rules to Correlations	1997	SIGMOD	0.00014713746
5,179	An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets	2009	PODS	6.3299352e-05

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