Decision Tables: Scalable Classification Exploring RDBMS Capabilities
Summary: Decision Tables builds a scalable classifier as decision tables, leveraging RDBMS primitives. Core work uses grouping and counting in native SQL, enabling fast training, easy implementation, and potential RDBMS gains; experiments show strong performance. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Hongjun Lu
- 2. Hongyan Liu
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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 | 0.0010864752 |
| 230 | An Interval Classifier for Database Mining Applications | 1992 | VLDB | 0.00032217064 |
| 247 | On the Computation of Multidimensional Aggregates | 1996 | VLDB | 0.00030927763 |
| 366 | An Array-Based Algorithm for Simultaneous Multidimensional Aggregates | 1997 | SIGMOD | 0.0002552977 |
| 1,107 | SPRINT: A Scalable Parallel Classifier for Data Mining | 1996 | VLDB | 0.00013985717 |
| 5,542 | NeuroRule: A Connectionist Approach to Data Mining | 1995 | VLDB | 5.4477778e-05 |
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