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Mining Association Rules between Sets of Items in Large Databases

Summary: Efficient algorithm to mine all significant association rules among itemsets in transaction databases. Introduces buffer management and novel estimation/pruning techniques; evaluation on a large retailer dataset shows scalability. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2703
Venue
SIGMOD
Year
1993
Pagerank
0.0006567919
Overall Rank
13 | 99.92%
DOI
10.1145/170035.170072

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{agrawal_sigmod93,
        title = {{Mining Association Rules between Sets of Items in Large Databases}},
        author = {Agrawal, Rakesh and Imielinski, Tomasz and Swami, Arun},
        series = {{SIGMOD} '93},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/170035.170072},
        url = {https://dl.acm.org/doi/10.1145/170035.170072},
        year = {1993}
}

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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
203 Dependency Inference 1987 VLDB 0.00025381538
440 An Interval Classifier for Database Mining Applications 1992 VLDB 0.00018409145
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