Given a data set with four transactions. Let min-support-60%, and min-confidence = 80% cust ID TID items_bought (in the form of brand-item category) 01 T100 (Sunny-Cherry, Dairyland-Milk, Wonder-Bread, Sweet-Pie) 02 T200 (Best-Cheese, Dairyland-Milk, Goldenfarm-Cherry, Sweet-Pie, Wonder-Bread 01 T300 [King's-Cereal, Sunset-Milk, Dairyland-Cheese, Best-Bread 03 T400 [Wonder-Bread, Sunset-Milk, Best-Cereal, Sweet-Pie, Dairyland-Cheese (a) At the granularity of item category (e.g. item, could be Milk and ignore brand name) for the following rule template, VX e transaction, buys(X, item 1) ^ buys(X, item2) buys(X, items) [s, c] list the frequent k-itemset for the largest k, and all of the strong association rules (with their support s and confidence c) containing the frequent k-itemset for the largest k (b) (Optional and 5-point Extra credit) At the granularity of brand-item_category (e.g., itemi could be "Sunset - Milk), for the following rule template, list the frequent k-itemset for the largest k (but do not print any rules) vX E customer, buys(X, itemi) A buys(X, item2) buys(x, items)