Data Mining and Warehousing mcq sppu unit 4

Data mining and warehousing mcq

1. What does Apriori algorithm do?

1. It mines all frequent patterns through pruning rules with lesser support
2. It mines all frequent patterns through pruning rules with higher support
3. Both a and b
4. None of these

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2. What does FP growth algorithm do?

1. It mines all frequent patterns through pruning rules with lesser support
2. It mines all frequent patterns through pruning rules with higher support
3. It mines all frequent patterns by constructing a FP tree
4. All of these

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3. What techniques can be used to improve the efficiency of apriori algorithm?

1. hash based techniques
2. transaction reduction
3. Partitioning
4. All of these

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4. What do you mean by support(A)?

1. Total number of transactions containing A
2. Total Number of transactions not containing A
3. Number of transactions containing A / Total number of transactions
4. Number of transactions not containing A / Total number of transactions

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5. Which of the following is direct application of frequent itemset mining?

1. Social Network Analysis
2. Market Basket Analysis
3. outlier detection
4. intrusion detection

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6. What is not true about FP growth algorithms?

1. It mines frequent itemsets without candidate generation
2. There are chances that FP trees may not fit in the memory
3. FP trees are very expensive to build
4. It expands the original database to build FP trees

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7. When do you consider an association rule interesting?

1. If it only satisfies min_support
2. If it only satisfies min_confidence
3. If it satisfies both min_support and min_confidence
4. There are other measures to check so

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8. What is the difference between absolute and relative support?

1. Absolute -Minimum support count threshold and Relative-Minimum support threshold
2. Absolute-Minimum support threshold and Relative-Minimum support count threshold
3. Both a and b
4. None of these

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9. What is the relation between candidate and frequent itemsets?

1. A candidate itemset is always a frequent itemset
2. A frequent itemset must be a candidate itemset
3. No relation between the two
4. None of these

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10. Which technique finds the frequent itemsets in just two database scans?

1. Patitioning
2. sampling
3. hashing
4. None of these

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Data mining and warehousing mcq sppu

11. Which of the following is true?

1. Both apriori and FP-Growth uses horizontal data format
2. Both apriori and FP-Growth uses vertical data format
3. Both a and b
4. None of these

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12. What is the principle on which Apriori algorithm work?

1. If a rule is infrequent, its specialized rules are also infrequent
2. If a rule is infrequent, its generalized rules are also infrequent
3. Both a and b
4. None of these

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13. Which of these is not a frequent pattern mining algorithm

1. Apriori
2. FP growth
3. Decision trees
4. Eclat

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14. Which algorithm requires fewer scans of data?

1. Apriori
2. FP growth
3. Both a and b
4. None of these

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15. What are Max_confidence, Cosine similarity, All_confidence?

1. Frequent pattern mining algorithms
2. Measures to improve efficiency of apriori
3. Pattern evaluation measure
4. None of these

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16. Linear regression – involves finding the________ line to fit two attributes (or variables)

1. An itemset for which at least one proper super itemset has same support
2. An item set whose no proper super- itemset has same support
3. Both a and b
4. None of these

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17. What are closed frequent itemsets?

1. A closed itemset
2. A frequent itemset
3. An itemset which is both closed and frequent
4. None of these

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18. What are maximal frequent itemsets?

1. A frequent item set whose no super-itemset is frequent
2. A frequent itemset whose super-itemset is also frequent
3. Both a and b
4. None of these

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19. Why is correlation analysis important?

1. To make apriori memory efficient
2. To weed out uninteresting frequent itemsets
3. To find large number of interesting itemsets
4. To restrict the number of database iterations

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data mining and warehousing mcq sppu

20. What will happen if support is reduced?

1. Number of frequent itemsets remains same
2. Some itemsets will add to the current set of frequent itemsets
3. Some itemsets will become infrequent while others will become frequent
4. Can not say

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21. Can FP growth algorithm be used if FP tree cannot be fit in memory?

1. Yes
2. No
3. Both a and b
4. None of these

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22. What is association rule mining?

1. Same as frequent itemset mining
2. Finding of strong association rules using frequent itemsets
3. Both a and b
4. None of these

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23. What is frequent pattern growth?

1. Same as frequent itemset mining
2. Use of hashing to make discovery of frequent itemsets more efficient
3. Mining of frequent itemsets without candidate generation
4. None of these

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24. When is sub-itemset pruning done?

1. A frequent itemset ‘P’ is a proper subset of another frequent itemset ‘Q’
2. Support (P) = Support(Q)
3. When both a and b is true
4. When a is true and b is not

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25. Which of the following is not null invariant measure(that does not considers null transactions)?

1. all_confidence
2. max_confidence
3. cosine measure
4. lift

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26. The apriori algorithm works in a ..and ..fashion?

1. top-down and depth-first
2. top-down and breath-first
3. bottom-up and depth-first
4. bottom-up and breath-first

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27. Our use of association analysis will yield the same frequent itemsets and strong association rules whether a specific item occurs once or three times in an individual transaction

1. TRUE
2. FALSE
3. Both a and b
4. None of these

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28. In association rule mining the generation of the frequent itermsets is the computational intensive step.

1. TRUE
2. FALSE
3. Both a and b
4. None of these

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29. The number of iterations in apriori __

1. increases with the size of the data
2. decreases with the increase in size of the data
3. increases with the size of the maximum frequent set
4. decreases with increase in size of the maximum frequent set

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30. Which of the following are interestingness measures for association rules?

1. recall
2. lift
3. accuracy
4. compactness

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data mining and warehousing mcq questions

31. Frequent item sets is

1. Superset of only closed frequent item sets
2. Superset of only maximal frequent item sets
3. Subset of maximal frequent item sets
4. Superset of both closed frequent item sets and maximal frequent item sets

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32. Assume that we have a dataset containing information about 200 individuals. A supervised data mining session has discovered the following rule: IF age < 30 & credit card insurance = yes THEN life insurance = yes Rule Accuracy: 70% and Rule Coverage: 63% How many individuals in the class life insurance= no have credit card insurance and are less than 30 years old?

1. 63
2. 30
3. 38
4. 70

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33. In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are

1. 100
2. 4950
3. 200
4. 5000

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34. Significant Bottleneck in the Apriori algorithm is

1. Finding frequent itemsets
2. pruning
3. Candidate generation
4. Number of iterations

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35. Which Association Rule would you prefer

1. High support and medium confidence
2. High support and low confidence
3. Low support and high confidence
4. Low support and low confidence

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36. The apriori property means

1. If a set cannot pass a test, its supersets will also fail the same test
2. To decrease the efficiency, do level-wise generation of frequent item sets
3. To improve the efficiency, do level-wise generation of frequent item sets
4. If a set can pass a test, its supersets will fail the same test

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37. If an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are

1. undefined
2. not frequent
3. frequent
4. cant say

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38. To determine association rules from frequent item sets

1. Only minimum confidence needed
2. Neither support not confidence needed
3. Both minimum support and confidence are needed
4. Minimum support is needed

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39. If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is

1. C –> A
2. D –> ABCD
3. A –> BC
4. B –> ADC

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