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Data warehouse and Data mining: Mining Frequent Patterns and Association Rules
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Data warehouse and Data mining: Mining Frequent Patterns and Association Rules
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17 Questions

1. For the question given below consider the data Transactions : I1, I2, I3, I4, I5, I6 - I7, I2, I3, I4, I5, I6 - I1, I8, I4, I5 - I1, I9, I10, I4, I6 - I10, I2, I4, I11, I5 With support as 0.6 find all frequent itemsets?"
2. Frequency of occurrence of an itemset is called as _____
3. What is the relation between a candidate and frequent itemsets?
4. A collection of one or more items is called as _____
5. What do you mean by support(A)?
6. When do you consider an association rule interesting?
7. What does FP growth algorithm do?
8. What will happen if support is reduced?
9. A definition or a concept is ______ if it classifies any examples as coming within the concept
10. Which of the following is the direct application of frequent itemset mining?
11. What is not true about FP growth algorithms?
12. How do you calculate Confidence (A -> B)?
13. What is association rule mining?
14. What techniques can be used to improve the efficiency of apriori algorithm?
15. An itemset whose support is greater than or equal to a minimum support threshold is ______
16. Which algorithm requires fewer scans of data?
17. Which of the following is not a frequent pattern mining algorithm?

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