Answered: A database has four transactions. Let…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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$A database has four transactions. Let min.sup = 60% and min.comf = 80%. cust ID items_bought (in the form of brand-item.category) {King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread} {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread} {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie} {Wonder-Bread, Sunset-Milk, Dairyland-Cheese} TID 01 T100 02 T200 01 T300 03 T400 (a) At the granularity of item.category (e.g., item; could be "Milk"), for the following rule template, VX € transaction, buys(X, item1) A buys(X, item2) = buys(X, item3) [s, c] list the frequent k-itemset for the largest k, and all of the strong association rules (with their support s and confidence c) containing the frequent k-itemset for the largest k. (b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following rule template, VX € customer, buys(X, item1) A buys(X, item2) = buys(X, item3) list the frequent k-itemset for the largest k (but do not print any rules).$

Transcribed Image Text:A database has four transactions. Let min.sup = 60% and min.comf = 80%. cust ID 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} TID 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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