Rule Generation

Use the data to generate all rules that satisfy a confidence threshold of = 0.8. The file contains frequent item sets ordered by size with their corresponding absolute support count. The transaction database that they came from had 100 transactions. For example, ('Beans', 'Milk'): 59 means that the frequent 2-item set ('Beans', 'Milk') has a relative support of 0.59.

Use the Apriori Rule Generation Algorithm in Excel:

All candidate rules generated with the -1 -1 strategy [4 Points]

All rules that remain after pruning [3 Points]

All final association rules for which and , including their support values and confidence values [3 Points]

Notes:

You may submit an Excel sheet that contains comprehensible formulas and detailed descriptions.

Simply providing final results without any intermediate calculation steps is insufficient and will be graded with 0 points!

Round all values to 4 decimal digits!

data:

Frequent 1-itemsets	Value
('Beans',)	94
('Chocolate',)	79
('Flour',)	98
('Milk',)	84
('Rice',)	80
('Spaghetti',)	84
Frequent 2-itemsets
('Beans', 'Chocolate')	39
('Beans', 'Flour')	80
('Beans', 'Milk')	59
('Beans', 'Rice')	51
('Beans', 'Spaghetti')	49
('Chocolate', 'Flour')	43
('Chocolate', 'Milk')	41
('Chocolate', 'Spaghetti')	67
('Flour', 'Milk')	72
('Flour', 'Rice')	56
('Flour', 'Spaghetti')	61
('Milk', 'Rice')	75
('Rice', 'Spaghetti')	60
Frequent 3-itemsets
('Beans', 'Chocolate', 'Spaghetti')	36
('Beans', 'Flour', 'Milk')	48
('Beans', 'Flour', 'Rice')	46
('Beans', 'Flour', 'Spaghetti')	42
('Beans', 'Milk', 'Rice')	48
('Beans', 'Rice', 'Spaghetti')	44
('Chocolate', 'Flour', 'Spaghetti')	38
('Flour', 'Milk', 'Rice')	55
('Flour', 'Rice', 'Spaghetti')	51
Frequent 4-itemsets
('Beans', 'Flour', 'Milk', 'Rice')	35
('Beans', 'Flour', 'Rice', 'Spaghetti')	41