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Tutorial 4

Tutorial 4. 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.

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Tutorial 4

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  1. Tutorial 4

  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 IF A B Support (AB)= #of tuples containing both (A,B) Total # of tuples • IF A B • Confidence (AB)= #of tuples containing both (A,B) Total # of tuples containing A

  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

  6. Minimum support • Minimum support=2/5= 40%

  7. example

  8. example {Egg, Milk} , {Egg, butter}  {Egg,Milk,butter} After that check all possible pairs in L2: {Egg,Milk}  ok {Egg,Butter}  ok {Milk,butter}  No Remove it

  9. cont • 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

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