A database has four transactions . Let min sup = 60 % and min conf = 80 %. TID Date Items_ bought 10/15 /19 {K, A,D,B ) 10/15 /19 [D,A, C.E.B) 300 10 /19 /19 {C,A, B, E } 400 10/22 /19 {B,A,D} (a) the frequent k-itemset for the largest k, and (b) all of the strong association rules (with support and confidence ) matching the following meta rule : v X 6 transaction ; buys (x; iteml ) ~ buys (x; item? ) ) buys (x; item3 ) [s; c]. A database has four transactions . Let min sup = 60 % and min conf = 509. CUST_ID TID items bought (in the form of brand -item category ) 100 100 (King's - Crab , Sunset -Milk , Dairyland - Cheese , Best - Bread } 200 Best - Cheese , Dairyland -Milk , Goldenfarm - Apple , Tasty - Pie , Wonder - Bread } 300 ( Westcoast - Apple , Dairyland -Milk , Wonder -Bread , Tasty - Pie } 401 400 (Wonder - Bread , Sunset -Milk , Dairyland -Cheese ) (a) At the granularity of item category (e.g., itemi could be "Milk "), for the following rule template , W X' 6 transaction ; buys (x; iteml ) ~ buys (x; item? ) ) buys (x; item3 ) [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) At the granularity of brand -item category (e.g., hemi could be " Sunset -Mik "), for the following rule template x 6 transaction ; buys (x; iteml ) * buys (x; item? ) ) buys (x; item3 ) [s; c]- list the frequent *-itemset for the largest & (but do not print any rules )