Given a data set with four transactions. Let min_support = 60%, and min_confidence = 80%.

cust_ID	TID	items_bought
01	T100	{Cherry, Milk, Bread, Pie}
02	T200	{Cheese, Milk, Cherry, Pie, Bread}
01	T300	{Cereal, Milk, Cheese, Bread}
03	T400	{Bread, Milk, Cereal, Pie, Cheese]

(a) At the granularity of item_category, for the following rule template:

X transaction, buys(X, item1item1) buys(X, item2item2) buys(X, item3item3) [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.

I have started working on the problem, and have worked out that:

01 T100 {Cherry, Milk, Bread, Pie} 02 T200 {Cheese, Milk, Cherry, Pie, Bread} 01 T300 {Cereal, Milk, Cheese, Bread} 03 T400 {Bread, Milk, Cereal, Cheese}

From these transactions, following itemsets can be drawn:

1-itemsets:

{Cherry}[s=50%] {milk}[s=100%] {cheese}[s=75%] {bread}[s=100%] {Cereal}[s=50%] {pie}[s=50%]

However, I am unsure how to go forward with calculating s and c for 2 item sets and 3 item sets, and completing the rest of the problem. I appreciate the help. Thanks!