Efficiently Mining Long Patterns from Databases
Summary: Pattern-mining algorithm scales linearly with the number of maximal patterns, independent of longest pattern length. Compared to Apriori, whose cost grows exponentially with pattern length, it achieves order-of-magnitude speedups for long patterns. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Roberto J. Bayardo Jr. (IBM)
BibTeX Citation
@inproceedings{jr_sigmod98,
title = {{Efficiently Mining Long Patterns from Databases}},
author = {Bayardo, Jr., Roberto J.},
series = {{SIGMOD} '98},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/276304.276313},
url = {https://dl.acm.org/doi/10.1145/276304.276313},
year = {1998}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 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 |
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 240 | An Effective Hash-Based Algorithm for Mining Association Rules | 1995 | SIGMOD | 0.0002366381 |
| 558 | An Efficient Algorithm for Mining Association Rules in Large Databases | 1995 | VLDB | 0.00016530269 |
| 704 | Dynamic Itemset Counting and Implication Rules for Market Basket Data | 1997 | SIGMOD | 0.00014837704 |
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