An online retailer has a database that stores 20,000 transactions of last month. After analyzing the data, a data science team has identified the following statistics: - { Milk\} appears in 18,000 transactions. - Cheese } appears in 16,000 transactions. - { Rice } appears in 15,000 transactions. - - Yogurt } appears in 14,000 transactions. - { Pasta } appears in 13,500 transactions. - Oil } appears in 12,000 transactions. - { Cereal } appears in 10,000 transactions. - { Pasta, Oil } appears in 11,500 transactions. - { Rice, Oil } appears in 9,500 transactions. - Milk, Cheese\} appears in 13,000 transactions. - { Milk, Cereal } appears in 10,000 transactions. - Milk, Yogurt } appears in 8,000 transactions. - { Milk, Cereal, Cheese\} appears in 8,500 transactions. - Milk, Cereal, Yogurt\} appears in 7,500 transactions. Applying Apriori algorithm, answer the following questions: a) What are the support values of the preceding itemsets? b) Assuming the minimum support is 0.04, which itemsets are considered frequent? c) What are the confidence values of the following rules: - { Milk }{ Cereal } - Milk, Cereal }{ Cheese } - Milk, Cereal, Cheese } Yogurt Which of the two rules is more interesting? Why? d) List all the candidate rules that can be formed from the statistics. Which rules are considered interesting at the minimum confidence 0.2 ? out of these interesting rules, which rule is considered the most useful (least coincidental)