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 (Hong Kong University of Science and Technology)
- 2. Hongyan Liu (Tsinghua University)
BibTeX Citation
@article{lu_vldb00,
title = {{Decision Tables: Scalable Classification Exploring RDBMS Capabilities}},
author = {Lu, Hongjun and Liu, Hongyan},
journal = {PVLDB},
series = {{VLDB} '00},
pages = {373--384},
year = {2000}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 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 | 0.00064420972 |
| 214 | On the Computation of Multidimensional Aggregates | 1996 | VLDB | 0.00024656893 |
| 323 | An Array-Based Algorithm for Simultaneous Multidimensional Aggregates | 1997 | SIGMOD | 0.0002100085 |
| 452 | An Interval Classifier for Database Mining Applications | 1992 | VLDB | 0.00018018759 |
| 1,519 | SPRINT: A Scalable Parallel Classifier for Data Mining | 1996 | VLDB | 0.00010393924 |
| 6,443 | NeuroRule: A Connectionist Approach to Data Mining | 1995 | VLDB | 5.782709e-05 |
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