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)
Incoming Non-self Citations Over Time
Authors
- 1. Rakesh Agrawal (IBM)
- 2. Tomasz Imielinski (IBM; Rutgers University)
- 3. Arun Swami (IBM)
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}
}
Incoming Citations (Sorted by Pagerank)
Showing 50 of 115 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
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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