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Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining

Summary: Introduces Ratio Rules, quantifiable associations scored by RMS “guessing error” for reconstructing hidden matrix cells. Enables forecasting, what-if analysis, outlier detection, and visualization in one pass with small memory—substantially lower error than standard methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h62f2b7687bf09cf0
Venue
VLDB
Year
1998
Pagerank
5.6715478e-05
Overall Rank
6,824 | 54.12%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{korn_vldb98,
        title = {{Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining}},
        author = {Korn, Flip and Labrinidis, Alexandros and Kotidis, Yannis and Faloutsos, Christos},
        journal = {PVLDB},
        series = {{VLDB} '98},
        year = {1998}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,084 Optimization of Constrained Frequent Set Queries with 2-variable Constraints 1999 SIGMOD 6.2834723e-05
8,976 Adding Counting Quantifiers to Graph Patterns 2016 SIGMOD 5.2452044e-05
9,697 Analyzing Quantitative Databases: Image is Everything 2001 VLDB 5.1387734e-05
13,170 Data Mining on an OLTP System (Nearly) for Free 2000 SIGMOD 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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