Mining association Rules Example of Rules TID tems Bread. milk MMilk, Diaper>Beer](s=0.4, C=0.67) Bread, Diaper, Beer, eggs MMilk, Beer] >Diaper)(s=0.4, C=1.0) Milk, Diaper, beer, Coke [Diaper, Beer]->Milk(s=0.4, C=0.67) [Beer]->Milk, Diaper](s=0.4, C=0.67) Bread, Milk, Diaper, Beer [Diaper]->Milk, Beer](s=0.4, C=0.5) Bread, Milk, Diaper, Coke MMilk>Diaper, Beer)(s=0.4, C=0.5) Observations All the above rules are binary partitions of the same itemset MIlk, Diaper, Beer] Rules originating from the same itemset have identical support but can have different confidence Thus, we may decouple the support and confidence requirements O Tan, Steinbach, Kumar Introduction to Data Mining 4/18/2004© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 ‹#› Mining Association Rules Example of Rules: {Milk,Diaper} → {Beer} (s=0.4, c=0.67) {Milk,Beer} → {Diaper} (s=0.4, c=1.0) {Diaper,Beer} → {Milk} (s=0.4, c=0.67) {Beer} → {Milk,Diaper} (s=0.4, c=0.67) {Diaper} → {Milk,Beer} (s=0.4, c=0.5) {Milk} → {Diaper,Beer} (s=0.4, c=0.5) TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke Observations: • All the above rules are binary partitions of the same itemset: {Milk, Diaper, Beer} • Rules originating from the same itemset have identical support but can have different confidence • Thus, we may decouple the support and confidence requirements