Consider the transactions shown in Table 7.14, with an item taxonomy given in Figure 7.25.
Table 7.14. Example of market basket transactions.

(a) What are the main challenges of mining association rules with item taxonomy?
(b) Consider the approach where each transaction t is replaced by an extended transaction t that contains all the items in t as well as their respective ancestors. For example, the transaction t = {Chips, Cookies} will be replaced by tʹ = {Chips, Cookies, Snack Food, Food}. Use this approach to derive all frequent itemsets (up to size 4) with support ‰¥ 70%.
(c) Consider an alternative approach where the frequent itemsets are generated one level at a time. Initially, all the frequent itemsets involving items at the highest level of the hierarchy are generated. Next, we use the frequent itemsets discovered at the higher level of the hierarchy to generate candidate itemsets involving items at the lower levels of the hierarchy. For example, we generate the candidate itemset {Chips, Diet Soda} only if {Snack Food, Soda} is frequent. Use this approach to derive all frequent itemsets (up to size 4) with support ‰¥ 70%.
(d) Compare the frequent itemsets found in parts (b) and (c). Comment on the efficiency and completeness of the algorithms.

Transaction ID tems Bought ps. C ookies, ffegular Soda, Ham Chips, Ham, Boneless Chicken, Diet Soda Ham, Bacon, Whole Chicken, Regular Soda Chips, Ham, Boneless Chicken, Diet Soda Chips, Bacon, Boneless Chicken Chips, Ham, Bacon, Whole Chicken, Regular Soda Chips, Cookies, Boneless Chicken, Diet Soda