1) If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Calculate the confidence of buying bread with jam
2) Consider the following dataset, use FP method to generate frequent patterns. Let min_support=2.
| TID | Items |
| 1 | I1,I2,I5 |
| 2 | I2, I4 |
| 3 | I2,I3 |
| 4 | I1,I2,I4 |
| 5 | I1,I3 |
| 6 | I2,I3 |
| 7 | I1,I3 |
| 8 | I1,I2,I3,I5 |
| 9 | I1,I2,I3 |
3) Consider the transaction database given below:
| TID | Items |
| 10 | Cookies,Ice-cream,Jelly |
| 20 | Fruits, Ice-cream,Butter |
| 30 | Cookies, Fruits, Ice-cream, Butter |
| 40 | Fruits, Butter |
| 50 | Cookies, Ice-cream, Butter |
Use Apriori algorithm with min support count =2 and min confidence=60% to find all frequent itemsets and strong association rules
4) GMART has database of transactions, consider the following subset of the dataset, use FP method to generate frequent patterns. Let min_support=2
| TID | Items |
| 1 | Chocolates,Bread,Cold Drink |
| 2 | Bread, Yogurt |
| 3 | Bread,Jam |
| 4 | Chocolates,Bread,Yogurt |
| 5 | Chocolates,Jam |
| 6 | Bread,Jam |
| 7 | Chocolates,Jam |
| 8 | Chocolates,Bread,Jam,Cold Drink |
| 9 | Chocolates,Bread,Jam |
5) Consider the following transaction database of all electronics company. Apply the Apriori algorithm with minimum support of 30% and minimum confidence of 75% and find all the association rules in the data set.
| TID | Items |
| 01 | Cookies, Bread, Milk, Juice |
| 02 | Chips, Bread, Chocolate, Soft Drink |
| 03 | Cookies, Chips, Bread, Chocolate, Eggs |
| 04 | Chips, Chocolate, Coffee, Tea |
| 05 | Cookies, Milk |
6) If T – 400000 transactions, 25000 transaction contain Tea, 50000 transaction contain Cookies, 15000 transaction contain both Tea and Cookies. Calculate the confidence of buying cookies given Tea.
7) Given the following data of a supermart, apply Apriori algorithm and find frequent itemset and generate association rules. Given support =50% and confidence = 60%
| Transaction | Items |
| T1 | Milk, Cheese,Yogurt |
| T2 | Cheese,Yogurt,Cookies |
| T3 | Cookies,Jam |
| T4 | Milk,Cheese,Cookies |
| T5 | Milk,Cheese,Yogurt,Jam |
| T6 | Milk,Cheese,Yogurt,Cookies |
8) Given the items of an e-commerce site, apply FP Growth tree mining algorithm to find frequent itemsets. Minimum support=3
| TID | Items |
| 1 | A, B, C, D, E, F |
| 2 | G, B, C, D, E, F |
| 3 | A, H, D, E |
| 4 | A, I, G, D, F |
| 5 | G, B, D, J, K |