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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
8657
Venue
VLDB
Year
1998
Pagerank
5.8014872e-05
Overall Rank
6,689 | 54.11%
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
4,969 Optimization of Constrained Frequent Set Queries with 2-variable Constraints 1999 SIGMOD 6.4201643e-05
8,809 Adding Counting Quantifiers to Graph Patterns 2016 SIGMOD 5.3655939e-05
9,515 Analyzing Quantitative Databases: Image is Everything 2001 VLDB 5.2567112e-05
12,880 Data Mining on an OLTP System (Nearly) for Free 2000 SIGMOD 5.093636e-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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