2. Association rule mining Goal: Find all rules that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf). Assume all data are categorical. No good algorithm for numeric data. Initially used for Market Basket Analysis to find how items purchased by customers are related.

3. Association rule An association rule is an implication of the form: X  Y, where X, Y  I, and X  Y =  Then:

4. The Apriori algorithm The best known algorithm. Two steps: Find all itemsets that have minimum support (frequent itemsets, also called large itemsets). Use frequent itemsets to generate rules.

5. Example Five transactions from a supermarket List of ItemsT id Egg,Butter,Baby Powder,Bread,Umbrella1 Butter,Baby Powder2 egg,Butter,Milk3 Butter,egg,chicken4 egg,Milk,Coca-Cola5

6. Minimum support SupportItem 4/5Egg 2/5Baby powder 1/5Umberilla 2/5Milk 1/5Bread 1/5Chicken 1/5Coca-Cola 4/5Butter Minimum support=2/5= 40% SupportItem 4/5Egg 2/5Baby powder 2/5Milk 4/5Butter

7. example SupportItem 1/5Egg,baby powder 2/5Egg,milk 3/5Egg,butter 0Baby powder,milk 2/5baby powder,Butter 1/5Milk,butter SupportItem 2/5Egg,milk 3/5Egg,butter 2/5baby powder,Butter

8. example SupportItem 1/5Egg,baby powder,butter 1/5Egg,milk,butter 0Egg,milk,baby powder 0Butter,Baby powder,milk Empty

9. cont ConfidenceSuport ASupport(A,B)Rules 75%80%60%Egg  Butter 50%80%40%Egg  Milk 50%80%40%Butter  Baby Powder 75%80%60%Butter  Egg 100%40% Milk  Egg 100%40% Baby Powder  Butter Minimum support=2/5= 40% min confidence=70%

10. Results Egg  Butter Support: 60% confidence:75% Butter  Egg Support: 60% confidence:75% Milk  Egg Support: 40% confidence:100% Baby Powder  Butter Support: 40% confidence:100%

11. Insert the same example to weka. Try the same example in Weka, insert marketing- list.csv

12. Reference: “Association Rules Apriori Algorithm”, https://dspace.ist.utl.pt/bitstream/2295/55704/1/licao _9.pdf https://dspace.ist.utl.pt/bitstream/2295/55704/1/licao _9.pdf