For this purpose, we further to define a measure as Equation (2): Where label_value(i) denotes the value of the i-th distance label (the value of [NEIGH], [NEAR], [ANY] is set to 2, 1, 0.5 separately), and k is the total number of distance labels used in rule r. Obviously, shorter rules with simpler distance constraint are more general and preferred.
step 1: step 2:  = Ds-sPM(<>, 0, s); step 3: for each  in  (a) count the frequencies of all covered classes (the classes that the covered sequences belong to), and find the most frequent class label yY; (b) if support(y)  min_sup and confidence(y)  min_conf then output y.