Help with DDP Mining algorithm for Effective Classification of data sets from 2 groups 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 &quot;0&quot; and group b has the class label &quot;1&quot; 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 &quot;branch and bound&quot; 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 &quot;most discriminative feature&quot;, i.e. the one which gives the most information about the class.