Data Mining and Knowledge Discovery

, Volume 4, Issue 2, pp 217–240

Constraint-Based Rule Mining in Large, Dense Databases

  • Roberto J. BayardoJr
  • Rakesh Agrawal
  • Dimitrios Gunopulos
Article

DOI: 10.1023/A:1009895914772

Cite this article as:
Bayardo, R.J., Agrawal, R. & Gunopulos, D. Data Mining and Knowledge Discovery (2000) 4: 217. doi:10.1023/A:1009895914772

Abstract

Constraint-based rule miners find all rules in a given data-set meeting user-specified constraints such as minimum support and confidence. We describe a new algorithm that directly exploits all user-specified constraints including minimum support, minimum confidence, and a new constraint that ensures every mined rule offers a predictive advantage over any of its simplifications. Our algorithm maintains efficiency even at low supports on data that is dense (e.g. relational tables). Previous approaches such as Apriori and its variants exploit only the minimum support constraint, and as a result are ineffective on dense data due to a combinatorial explosion of “frequent itemsets”.

data mining association rules rule induction 

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Roberto J. BayardoJr
    • 1
  • Rakesh Agrawal
    • 2
  • Dimitrios Gunopulos
    • 3
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA
  3. 3.IBM Almaden Research CenterSan JoseUSA

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