Journal of Intelligent Information Systems

, Volume 20, Issue 3, pp 255–283 | Cite as

A Statistical Theory for Quantitative Association Rules



Association rules are a key data-mining tool and as such have been well researched. So far, this research has focused predominantly on databases containing categorical data only. However, many real-world databases contain quantitative attributes and current solutions for this case are so far inadequate. In this paper we introduce a new definition of quantitative association rules based on statistical inference theory. Our definition reflects the intuition that the goal of association rules is to find extraordinary and therefore interesting phenomena in databases. We also introduce the concept of sub-rules which can be applied to any type of association rule. Rigorous experimental evaluation on real-world datasets is presented, demonstrating the usefulness and characteristics of rules mined according to our definition.

data mining knowledge discovery in data bases quantitative association rules statistical inference theory 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat GanIsrael
  2. 2.IBM T.J. Watson ResearchHawthorneUSA

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