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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
2642
Venue
SIGMOD
Year
1993
Pagerank
0.0010863639
Overall Rank
13 | 99.92%
DOI
-

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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
25 Dependency Inference (Extended Abstract) 1987 VLDB 0.00083070519
232 An Interval Classifier for Database Mining Applications 1992 VLDB 0.00032060702
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