Formally, let F = {f1, f2, … , fn} be a set of n disjoint items/features and C be a list of classes {c1, c2, .., cm}. A transaction database D is a set of transaction where each transaction/compound t contains a set of items f1, f2, … , fk∈F and cj∈ C. In this study, a transaction is a compound which, for example, can be represented by {4, 5, 7,…, active}. 4, 5, and 7 etc can be the MDL features and "active" is the labeled class. Let s is a set of items with 𝑠 ⊆ 𝐹 ∪ 𝐶. s is referred to as an itemset. The support of an itemset s is the percentage of transactions in D that contain s. An itemset is frequent if only if s is contained in the training set D and satisfies a minimum support θ (called minsup) threshold. A CAR has the form of implication as (Equation ): X⇒Y or X ⟶Y where X ⊆ F and Y ∈ C