Static Discretization of quantitative Attributes Discretized prior to mining using concept hierarchy Numeric values are replaced by ranges In relational database, finding all frequent k-predicate sets will require k or k+1 table scans Data cube is well suited for mining The cells of an n-dimensional age (income) buys cuboid correspond to the predicate sets Mining from data cubes (age, income)(age, buys)(ineome, buys can be much faster (age, income, buys) 1111 Static Discretization of Quantitative Attributes ◼ Discretized prior to mining using concept hierarchy. ◼ Numeric values are replaced by ranges ◼ In relational database, finding all frequent k-predicate sets will require k or k+1 table scans ◼ Data cube is well suited for mining ◼ The cells of an n-dimensional cuboid correspond to the predicate sets ◼ Mining from data cubes can be much faster (age) (income) () (buys) (age, income) (age,buys) (income,buys) (age,income,buys)