Answered: 1)Use Python to solve questions A…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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$1)Use Python to solve questions A database has 5 transactions. Let min_sup = 60% and min.conf 80%. TID items.bought {М, О, N, K, E, Y} {D, O, N, K, E, Y } {М, А, К, Е} {M, U, C, K, Y} {С, О, О, К, I,E} T100 T200 T300 T400 Т500 (a) Find all frequent itemsets using Apriori (b) List all of the strong association rules (with supports and confidence c) matching the following metarule, where X is a variable representing customers, and item; denotes variables representing items (e.g., "A", "B", etc.): Vx E transaction, buys(X, item1) A buys(X, item2) = buys(X, item3) [s, c] 2)Use Python to solve questions A database has four transactions. Let min_sup = 60% and min.conf = 80%. cust ID items_bought (in the form of brand-item.category) {King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread} {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread} {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie} {Wonder-Bread, Sunset-Milk, Dairyland-Cheese} TID 01 T100 02 T200 01 T300 03 T400 (a) At the granularity of item.category (e.g., item; could be "Milk"), for the following rule template, VX e transaction, buys(X, itemı) A buys(X, item2) = buys(X, item3) [s, c] list the frequent k-itemset for the largest k, and all of the strong association rules (with their support s and confidence c) containing the frequent k-itemset for the largest k. (b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following rule template, X € customer, buys(X, item1) A buys(X, item2) buys(X, item3) list the frequent k-itemset for the largest k (but do not print any rules).$

Transcribed Image Text:1)Use Python to solve questions A database has 5 transactions. Let min_sup = 60% and min.conf 80%. TID items.bought {М, О, N, K, E, Y} {D, O, N, K, E, Y } {М, А, К, Е} {M, U, C, K, Y} {С, О, О, К, I,E} T100 T200 T300 T400 Т500 (a) Find all frequent itemsets using Apriori (b) List all of the strong association rules (with supports and confidence c) matching the following metarule, where X is a variable representing customers, and item; denotes variables representing items (e.g., "A", "B", etc.): Vx E transaction, buys(X, item1) A buys(X, item2) = buys(X, item3) [s, c] 2)Use Python to solve questions A database has four transactions. Let min_sup = 60% and min.conf = 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 e transaction, buys(X, itemı) 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, X € 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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