Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Association Rules

  • Jian Pei
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_26

Definition

Let I be a set of items, where each item is a literal. A transaction TI is a subset of I. Association rules are defined on a set of transactions T.

An association rule R is in the form of X → Y, where X and Y are two sets of items, that is, X , YI. R is associated with two measures, the support sup(R) and the confidence conf (R). The support sup(R) is the probability that X appears in a transaction in T. The confidence conf (R) is the conditional probability that when X appears in a transaction, Y also appears.

Historical Background

The concept of association rules was firstly proposed by Agrawal et al. [1] for market basket analysis. A well-known illustrative example of association rules is “Diaper → Beer” which can be explained by the fact that when dads buy diapers for their babies, they also buy beer at the same time for their weekends game watching.

Apriori, an efficient algorithm for mining association rules, was developed by Agrawal and Srikant [2], while the...

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Recommended Reading

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

Section editors and affiliations

  • Jian Pei
    • 1
  1. 1.School of Computing ScienceSimon Fraser Univ.BurnabyCanada