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Online Association Rule Mining

Summary: Online association rule mining; single-pass online large-itemset discovery with dynamic threshold changes. Maintains a superset of large itemsets; uses shrinking intervals; finishes after 2 scans with exact supports, saving memory vs Apriori/DIC. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3157
Venue
SIGMOD
Year
1999
Pagerank
6.7479905e-05
Overall Rank
4,354 | 70.13%
DOI
10.1145/304182.304195

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{hidber_sigmod99,
        title = {{Online Association Rule Mining}},
        author = {Hidber, Christian},
        series = {{SIGMOD} '99},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/304182.304195},
        url = {https://dl.acm.org/doi/10.1145/304182.304195},
        year = {1999}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
122 Approximate Frequency Counts over Data Streams 2002 VLDB 0.00031260115
5,895 The 3W Model and Algebra for Unified Data Mining 2000 VLDB 6.0486927e-05
6,017 Data Mining with the SAP NetWeaver BI Accelerator 2006 VLDB 6.0061791e-05
6,454 An Optimal Algorithm for l1-Heavy Hitters in Insertion Streams and Related Problems 2016 PODS 5.8746921e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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