Parallel Mining Algorithms for Generalized Association Rules with Classification Hierarchy
Summary: Parallel mining for generalized association rules with a classification hierarchy on a shared-nothing machine; partitioning minimizes interprocessor communication. Also exploits frequent-itemset replication to balance load, with IBM SP-2 experiments showing speedups. (summarized by gpt-5-nano on Feb 09 2026)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,131 | Dynamic Load Balancing for Parallel Association Rule Mining on Heterogeneous PC Cluster System | 1999 | VLDB | 4.3899185e-05 |
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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 |
|---|---|---|---|---|
| 36 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00076161096 |
| 403 | Mining Generalized Association Rules | 1995 | VLDB | 0.00024148455 |
| 4,175 | Scalable Parallel Data Mining for Association Rules | 1997 | SIGMOD | 6.3851209e-05 |
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| 12,462 | Optimization of Frequent Itemset Mining on Multiple-Core Processor | 2007 | VLDB | 4.1945683e-05 |
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| 547 | An Efficient Algorithm for Mining Association Rules in Large Databases | 1995 | VLDB | 0.00020420717 |
| 9,131 | Dynamic Load Balancing for Parallel Association Rule Mining on Heterogeneous PC Cluster System | 1999 | VLDB | 4.3899185e-05 |
| 4,175 | Scalable Parallel Data Mining for Association Rules | 1997 | SIGMOD | 6.3851209e-05 |