Association Rule Confidence

An Association Rule Confidence is a Proportion for an Association Rule where the Cardinality of the rule's resulting set is divided by the antecedent set.

• AKA: Confidence.
• See: Itemset Support, Minimum Association Rule Confidence, Association Rule Learning Task.

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References

2009

• http://en.wikipedia.org/wiki/Association_rule_learning#Useful_Concepts
  • The confidence of a rule is defined [math]\displaystyle{ \mathrm{conf}(X\Rightarrow Y) = \mathrm{supp}(X \cup Y) / \mathrm{supp}(X) }[/math]. For example, the rule [math]\displaystyle{ \{\mathrm{milk, bread}\} \Rightarrow \{\mathrm{butter}\} }[/math] has a confidence of [math]\displaystyle{ 0.2/0.4=0.5 }[/math] in the database, which means that for 50% of the transactions containing milk and bread the rule is correct.
  • Confidence can be interpreted as an estimate of the probability [math]\displaystyle{ P(Y|X) }[/math], the probability of finding the RHS of the rule in transactions under the condition that these transactions also contain the LHS.

2007

• Howard Hamilton. (2007). “[1] Computer Science 831: Knowledge Discovery in Databases, University of Regina.
  • Confidence of rule X ® Y, denoted conf(X ® Y)
    • conf(X ® Y) = supp(X È Y) / supp(X)
    • Confidence can also be defined in terms of the conditional probability.
      • conf(X ® Y) = P(Y | X) = P(X Ç Y) / P(X)

2000

• (Hipp et al., 2000) ⇒ Jochen Hipp, Ulrich Güntzer, and Gholamreza Nakhaeizadeh. (2002). “Algorithms for Association Rule Mining - A general survey and comparison.” In: SIGKDD Explorations, 2(2).

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