min_conf = 80%: (a) Find all frequent itemsets (not just the ones with the maximum width/length) using the Apriori algorithm. Show your work—just showing the final answer is not acceptable. For each iteration show the candidate and acceptable frequent itemsets. You should show your work similar to the way the example was done in the PowerPoint slides. (b)List all of the strong association rules, along with their support and confidence values, whichmatch the following metarule, where X is a variable representing customers and itemi denotes variables representing items (e.g., “A”, “B”, etc.). x transaction, buys(X, item1) buys(X, item2) buys(X, item3) Hint: don’t worry about the fact that the statement above uses relations. The point of the metarule is to tell you to only worry about association rules of the form X Y Z (or {X, Y} Z if you prefer that notation). That is, you don’t need to worry about rules of the form X Z.
A
TID Date items_bought
T100 10/15/04 {K, A, D, B}
T200 10/15/04 {D, A, C, E, B}
T300 10/19/04 {C, A, B, E}
T400 10/22/04 {B, A, D}
Assuming a minimum level of support min_sup = 60% and a minimum level of
confidence
min_conf = 80%:
(a) Find all frequent itemsets (not just the ones with the maximum width/length) using
the Apriori
acceptable. For each iteration show the candidate and acceptable frequent itemsets.
You should show your work similar to the way the example was done in the
PowerPoint slides.
(b)List all of the strong association rules, along with their support and confidence
values, whichmatch the following metarule, where X is a variable representing
customers and itemi denotes variables representing items (e.g., “A”, “B”, etc.).
x transaction, buys(X, item1) buys(X, item2) buys(X, item3)
Hint: don’t worry about the fact that the statement above uses relations. The point of
the metarule is to tell you to only worry about association rules of the form X Y
Z (or {X, Y} Z if you prefer that notation). That is, you don’t need to worry
about rules of the form X Z.
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