Question

A database has four transactions. Let min_sup=60% and min_conf=80%.Cust_Id TID Items bought (in the form of brand-item_category)01 T100 {King’s-Crab, Sunset-Milk, Dairy land-Cheese, Best-Bread}02 T200 {Best-Cheese, Dairy land-Milk, Golden farm-Apple, Tasty- Pie. Wonder-Bread}01 T300 {West coast-Apple, Dairy land- Milk, Wonder- Bread, Tasty-Pie}03 T400 {Wonder-Bread, Sunset-Milk, Dairy land-Cheese}(a) Find all frequent item sets using Apriori algorithm​

Answer 1

All frequent item sets using Apriori Algorithm​:

• At  the  granularity  of item category(e.g., $item_{i}$ could  be  “Milk”)  for  the following rule template.

∀X ∈ transaction, buys(X, item1) ∧ buys(X, item2) ⇒buys(X, item3) [s, c]

List  the  frequent k-itemset  for  the  largest k,  and all of  the strong association rules (with their support sand confidence c) containing the frequent k-itemset for the largest k).

• At the granularity of brand-item category(e.g., $item_{i}$, could be “Sunset-Milk”), for the following rule template.

∀X∈ customer, buys(X, item1) ∧buys(X, item2) ⇒buys(X, item3)

List the frequent k-itemset for the largest k (but do not print any rules).

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