ACCTG 6910, Spring 2003
DESB, University of Utah
Assignment 3 (3/27 – 4/8)
Question 1(50 points): Given the following transactions and minimum support - 50% and minimum confidence - 80% large item sets, sequential patterns, rules, lifts, recommend some management decisions
TID
/Brand_Item_bought
100 / King’s-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread200 / Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread
300 / Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie
400 / Wonder-Bread, Sunset-Milk, Dairyland-Cheese
a) At the granularity of item without brand (e.g., “milk” and “bread”), please identify all large itemsets using the Apriori algorithm. Be sure to include all steps in Apriori, i.e., Large (k-1)-itemset à Candidate k-itemset (Join, Prune) à Large k-itemset.
b) At the granularity of brand-item (e.g., “Sunset-Milk” and “Wonder-Bread”), please identify all large itemsets using the Apriori algorithm. Be sure to include all steps in Apriori, i.e., Large (k-1)-itemset à Candidate k-itemset (Join, Prune) à Large k-itemset.
c) Please list all association rules (i.e., association rules that meet minimum support and minimum confidence requirements) derived from the itemsets you derived in b) and their supports, confidences and lifts.
d) Please give one recommendation (e.g., store layout or promotion) to store management based on the association rules and large item sets you discovered.
Question 2 (25 points): Let the minimum support be 60% when you derive large sequences from the following transaction database.
A / 100 / 1,2
A / 200 / 3,4
A / 300 / 5,6
A / 400 / 1,2
B / 500 / 1
B / 600 / 3
B / 700 / 5
B / 800 / 1
C / 900 / 2
C / 1000 / 4
C / 1100 / 6
C / 1200 / 2
a) Please identify all large sequencies using the Apriori algorithm. Be sure to include all steps in Apriori, i.e., Large (k-1)-sequences à Candidate k-sequencies (Join, Prune) à Large k-sequences.
Question3 (25 points): Go to an ecommerce web site such as amazon.com or buy.com. Discover and describe one application of the use association rules or sequential patterns. Please comment on whether it is effective or needs improvement.