Support The support defined as the proportion of transaction in the database, which contains the items A  $$Support(A \to B) = \frac{|t(A \cup B)|}{t(A)}$$ (1)
Confidence The confidence determines how frequently items in B appear in transaction that contains A , ranges from 0 to 1 $$Confidence(A \to B) = \frac{Support(A \cup B)}{Support(A)}$$ (2)
Lift The lift measures how far from independence are A and B . It ranges within [0, +∞] $$Lift(A \to B) = \frac{Support(A \cup B)}{Support(A)*Support(B)}$$ (9)
Laplace It is a confidence estimator that takes support into account . It ranges within [0, 1] $$lapl(A \to B) = \frac{Support(A \cup B) + 1}{Support(A) + 2}$$ (10)
Conviction Measure the degree of implication of a rule . It ranges along the values [0.25, +∞] $$conv(A \to B) = \frac{1 - Support(B)}{1 - conf(A \to B)}$$ (11)
Leverage Measure how much more counting is obtained from the co-occurrence of the antecedent and consequent from the independence  $$leve\left( {A \to B} \right) = Support\left( {A \to B} \right) - Support\left( A \right) \, \times \, Support\left( B \right)$$ (12)
Jaccard Measure the degree of overlap between the cases covered by each of them  the Jaccard coefficient takes values in [0, 1] $$Jacc(A \to B) = \frac{Support(A \cup B)}{Support(A)\, + \,Support(B) - Support(A \cup B)}$$ (13)
ϕ-Coefficient This measure can be used to measure the association between A and B  $$\phi (A \to B) = \frac{leve(A \cup B)}{{\sqrt {(Support(A)\, \times \,Support(B))\, \times \,(1 - Support(A))\, \times \,(1 - Support(B)} )}}$$ (14)