QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules

Background

Rule learning has been mainly addressed from two different perspectives: predictive and descriptive tasks. Rule learning in predictive tasks is mainly concerned in generating classification rules that form a classifier. On the other hand, rule generation in descriptive tasks focus in finding all rules over a certain confidence that summarizes the data. However, in a broader sense, rules (either predictive or descriptive) can be considered as an association of two binary variables: the rule antecedent and the rule consequent. Rules of the formantecedent → consequent,where both antecedent and consequent are conjunctions of features (for classification rules, the consequent always refers to a single feature), whereas they do not have features in common. The antecedent is also called left-hand side, premise, condition, tail or body and the consequent is called right-hand side, conclusion or head. Throughout this chapter, we will also use the general notation X → Y (X corresponding to the antecedent and Y to the consequent, respectively) to denote a rule.