I'm trying to implement the DDPmine algorithm from this article as part of some project, and I do not understand where in the algorithm we use the Class Label of each transaction? We have transactions from 2 different groups spouse group has a class label "0" and group b has the class label "1" and we want to find the Discriminative Patterns that are frequent in each group but not on the 2 groups combined but in which part of the algorithm we consider this? what I'm missing here?
I didn't read the paper in detail but I can see that the class label is used in Algorithm 2 "branch and bound" p. 3: it's not very clear because it's only used through Information Gain (IG). I assume that the IG is calculated between the feature and the class, in order to find the "most discriminative feature", i.e. the one which gives the most information about the class.