Representative association rules and minimum condition maximum consequence association rules

  • Marzena Kryszkiewicz
Posters

DOI: 10.1007/BFb0094839

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1510)
Cite this paper as:
Kryszkiewicz M. (1998) Representative association rules and minimum condition maximum consequence association rules. In: Żytkow J.M., Quafafou M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg

Abstract

Discovering association rules (AR) among items in a large database is an important database mining problem. The number of association rules may be large. To alleviate this problem, we introduced in [1] a notion of representative association rules (RR. RR is a least set of rules that covers all association rules. The association rules, which are not representative ones, may be generated by means of a cover operator without accessing a database. On the other hand, a subset of association rules that allows to predict as much as possible from minimum facts is usually of interest to analysts. This kind of rules we will call minimum condition maximum consequence rules (MMR). In this paper, we investigate the relationship between RR and MMR. We prove that MMR is a subset of RR and it may be extracted from RR.

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Copyright information

© Springer-Verlag 1998

Authors and Affiliations

  • Marzena Kryszkiewicz
    • 1
  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarsawPoland

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