Mining Quantitative Associations Techniques can be categorized by how numerical attributes such as age or salary are treated 1. Static discretization based on predefined concept hierarchies(data cube methods) 2. Dynamic discretization based on data distribution (quantitative rules eg Agrawal srikant@SIGMOD96 3. Clustering: Distance-based association(e.g. Yang Miller@SIGMOD97 One dimensional clustering then association 4. Deviation:(such as Aumann and Lindell@KDD99) Sex= female = Wage: mean=$7/hr(overall mean= $9)10 Mining Quantitative Associations Techniques can be categorized by how numerical attributes, such as age or salary are treated 1. Static discretization based on predefined concept hierarchies (data cube methods) 2. Dynamic discretization based on data distribution (quantitative rules, e.g., Agrawal & Srikant@SIGMOD96) 3. Clustering: Distance-based association (e.g., Yang & Miller@SIGMOD97) ◼ One dimensional clustering then association 4. Deviation: (such as Aumann and Lindell@KDD99) Sex = female => Wage: mean=$7/hr (overall mean = $9)