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Data Mining and Business Intelligence: Frequent Patterns
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Data Mining and Business Intelligence: Frequent Patterns
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17 Questions

1. What does FP growth algorithm do?
2. Which algorithm requires fewer scans of data?
3. For the question given below consider the data Transactions:
I1, I2, I3, I4, I5, I6I7, I2, I3, I4, I5, I6I1, I8, I4, I5I1, I9, I10, I4, I6I10, I2, I4, I11, I5 With support as 0.6 find all frequent itemsets?
4. A collection of one or more items is called as _____
5. A definition or a concept is ______ if it classifies any examples as coming within the concept
6. What will happen if support is reduced?
7. What is not true about FP growth algorithms?
8. What techniques can be used to improve the efficiency of apriori algorithm?
9. Frequency of occurrence of an itemset is called as _____
10. What do you mean by support(A)?
11. What is the relation between a candidate and frequent itemsets?
12. Which of the following is not a frequent pattern mining algorithm?
13. Which of the following is the direct application of frequent itemset mining?
14. When do you consider an association rule interesting?
15. An itemset whose support is greater than or equal to a minimum support threshold is ______
16. How do you calculate Confidence (A -> B)?
17. What is association rule mining?

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