Minimum confidence and minimum support for Apriori

algorithm parameters data-mining apriori
lmsasu picture lmsasu · Jan 5, 2010 · Viewed 10.9k times · Source

What are appropriate values for minimum confidence and minimum support values for the Apriori algorithm? How could you tweak them? Are they fixed values, or do they change during the running of the algorithm? If you have used this algorithm before, what values did you use?

Answer

George Dontas picture George Dontas · Jan 7, 2010

I would suggest to start with values 0.05 for support and 0.80 for confidence. But I agree that you should understand what exactly they represent in order to be able to define them appropriately. For a rule A => B (where A, B non empty sets) 

Support (A ⇒ B): s = P(A, B)
Confidence (A ⇒ B): c = P(B | A)
Lift (A ⇒ B): L = c/P(B)

Lift is important to assess the interestingness of a rule (because you usually come up with hundreds of them). More than twenty measures of interestingness have been proposed. These include the Ф-coefficient, kappa, mutual information, the J-measure and the Gini index. I personaly order my rules according to the J-measure.

J.measure (A ⇒B): J = s/c * (c*log(L) + (1-c)*log((L-c)/L))

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