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)

Paper ID: 8854
Venue: VLDB
Year: 2000
Pagerank: 5.093636e-05
Overall Rank: 12,885 | 11.60%
DOI: -

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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	0.0006567919
207	On the Computation of Multidimensional Aggregates	1996	VLDB	0.00025088003
324	An Array-Based Algorithm for Simultaneous Multidimensional Aggregates	1997	SIGMOD	0.00021200835
440	An Interval Classifier for Database Mining Applications	1992	VLDB	0.00018409145
1,485	SPRINT: A Scalable Parallel Classifier for Data Mining	1996	VLDB	0.00010628998
6,322	NeuroRule: A Connectionist Approach to Data Mining	1995	VLDB	5.9147876e-05

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