2) A database has four transactions. Let min sup = 60% and min.conf = 80%. %3D TID items_bought (in the form of brand-item.category) {King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread} {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread} {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie} {Wonder-Bread, Sunset-Milk, Dairyland-Cheese} cust ID 01 T100 02 T200 01 Т300 03 T400 (a) At the granularity of item_category (e.g., item; could be "Milk"), for the following rule template, VX € transaction, buys(X, item1) A 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 s and confidence c) containing the frequent k-itemset for the largest k. (b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following rule template, VX € customer, buys(X, item1) A buys(X, item2) = buys(X, item3) list the frequent k-itemset for the largest k (but do not print any rules).
2) A database has four transactions. Let min sup = 60% and min.conf = 80%. %3D TID items_bought (in the form of brand-item.category) {King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread} {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread} {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie} {Wonder-Bread, Sunset-Milk, Dairyland-Cheese} cust ID 01 T100 02 T200 01 Т300 03 T400 (a) At the granularity of item_category (e.g., item; could be "Milk"), for the following rule template, VX € transaction, buys(X, item1) A 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 s and confidence c) containing the frequent k-itemset for the largest k. (b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following rule template, VX € customer, buys(X, item1) A buys(X, item2) = buys(X, item3) list the frequent k-itemset for the largest k (but do not print any rules).
Chapter2: Database Design Fundamentals
Section: Chapter Questions
Problem 11RQ
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i need the answer quickly
![2)
A database has four transactions. Let min sup = 60% and min.conf
= 80%.
%3D
TID
items_bought (in the form of brand-item.category)
{King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread}
{Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread}
{Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie}
{Wonder-Bread, Sunset-Milk, Dairyland-Cheese}
cust ID
01
T100
02
T200
01
T300
03
T400
(a) At the granularity of item_category (e.g., item; could be "Milk"), for the following rule template,
VX € transaction, buys(X, item1) A 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 s and confidence c) containing the frequent k-itemset for the largest k.
(b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following
rule template,
VX € customer, buys(X, item1) A buys(X, item2) = buys(X, item3)
list the frequent k-itemset for the largest k (but do not print any rules).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd8925810-efc0-4b46-af3b-eb68e6bbcc5a%2Faa7ee397-1e09-44ea-b767-ff03f886dbf1%2Fhjt4jjd_processed.jpeg&w=3840&q=75)
Transcribed Image Text:2)
A database has four transactions. Let min sup = 60% and min.conf
= 80%.
%3D
TID
items_bought (in the form of brand-item.category)
{King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread}
{Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread}
{Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie}
{Wonder-Bread, Sunset-Milk, Dairyland-Cheese}
cust ID
01
T100
02
T200
01
T300
03
T400
(a) At the granularity of item_category (e.g., item; could be "Milk"), for the following rule template,
VX € transaction, buys(X, item1) A 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 s and confidence c) containing the frequent k-itemset for the largest k.
(b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following
rule template,
VX € customer, buys(X, item1) A 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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