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)
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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 |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.0010863639 |
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076114894 |
| 117 | An Effective Hash-Based Algorithm for Mining Association Rules | 1995 | SIGMOD | 0.00045914132 |
| 546 | An Efficient Algorithm for Mining Association Rules in Large Databases | 1995 | VLDB | 0.00020410312 |
| 651 | Dynamic Itemset Counting and Implication Rules for Market Basket Data | 1997 | SIGMOD | 0.00018649942 |
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| 10,978 | Language-Model Based Informed Partition of Databases to Speed Up Pattern Mining | 2024 | SIGMOD | 4.1905499e-05 |
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076114894 |
| 546 | An Efficient Algorithm for Mining Association Rules in Large Databases | 1995 | VLDB | 0.00020410312 |
| 182 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.00036955562 |