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Data Mining MCQs - Unit-2
1. Which measure represents how often an itemset appears in the dataset?
A . Confidence
B . Support
C . Lift
D . Correlation
2. Which algorithm is used for frequent itemset mining?
A . Decision tree algorithm
B . K-nearest neighbors algorithm
C . Apriori algorithm
D . Naive Bayes algorithm
3. The measure that shows reliability of an association rule is
A . Support
B . Confidence
C . Lift
D . Correlation
4. An itemset whose support is greater than or equal to a minimum support threshold is ___________________
A . Itemset
B . Frequent Itemset
C . Infrequent items
D . Threshold values
5. Which of the following is a data reduction technique?
A . Clustering
B . Classification
C . Sampling
D . Regression
6. What does FP growth algorithm do?
A . It mines all frequent patterns through pruning rules with lesser support
B . It mines all frequent patterns through pruning rules with higher support
C . It mines all frequent patterns by constructing a FP tree
D . It mines all frequent patterns by constructing an itemsets
7. What do you mean by support (A)?
A . Total number of transactions containing A
B . Total Number of transactions not containing A
C . Number of transactions containing A / Total number of transactions
D . Number of transactions not containing A / Total number of transactions
8. Which of the following is not a measure of association used in association rule mining?
A . Support
B . Confidence
C . Lift
D . Entropy
9. Rule: “Age(20–29) ∧ Occupation(Student) => Buys(Laptop)” is
A . Single dimensional
B . Multidimensional
C . Quantitative
D . Hybrid dimensional
10. Which algorithm requires multiple scans of data?
A . Apriori
B . FP Growth
C . Eclat
D . Decision Trees
11. Market basket analysis is an example of_____________
A . Classification
B . Clustering
C . Association rule mining
D . Outlier detection
12. Which of the following is the direct application of frequent itemset mining?
A . Social Network Analysis
B . Market Basket Analysis
C . Outlier Detection
D . Intrusion Detection
13. Which of the following is not a type of attribute used in data mining?
A . Nominal
B . Ordinal
C . Interval
D . Decimal
14. The step in Apriori where infrequent candidates are removed is
A . Join step
B . Prune step
C . Mining step
D . None
15. Apriori property states that
A . All supersets of infrequent itemset will be infrequent
B . All subsets of infrequent itemset must be frequent
C . All supersets of a frequent itemset must be frequent
D . None of the above
16. ______________________ Association Rule mining is used to discover relationships between items at different levels of granularity.
A . Multilevel
B . MultiDimensional
C . Quantative
D . None of the above
17. The data structure used in FP-Growth algorithm is
A . Hash table
B . FP-tree
C . Graph
D . Array
18. Which of the following is not a type of correlation?
A . Positive
B . Negative
C . Null
D . Zero
19. The interesting patterns are presented to the user and may be stored as new knowledge in the ______.
A . Database
B . Repository
C . Knowledge base
D . Process
Fill in the Blanks
20. ____________________ is a popular form of background knowledge, which allows data to be mined at multiple levels.
21. The FP Growth algorithm is a popular method for frequent pattern mining in data mining. It works by constructing ________________
22. How do you calculate Confidence (A -> B)? ___________________________________
23. How do you calculate Lift {Bread -> Milk}? ____________________________________
24. Correlation Analysis is a data mining technique used to identify ___________________
25. The ________________ is not suitable for handling large datasets because it generates a large number of candidates.
26. The FP-tree (Frequent Pattern tree) is a data structure used in the FP Growth algorithm that stores the ______________ and _____________________.
27. Association rules that involve two or more dimensions or predicates can be referred to as ____________________________________.
28. Multilevel association rules can be mined efficiently using ____________________________.
29. Association rules that involve single dimension or predicate can be referred to as a ______________________ association rule.
30. Quantitative association rules having __________________ on the left-hand side and ______________________ on the right-hand side of the rule.
31. Steps in Apriori algorithm are ____________________ and __________________
32. If there is a pair of items, X and Y, which are frequently bought together then association rule is represented as _______________________.
33. Positive correlation exists when both variables move in the _______ direction.
Relevant Materials :
☞ Data Mining MCQs - Unit-1 - [ DM ]
☞ Data Mining MCQs - Unit-2 - [ DM ]
☞ Data Mining MCQs - Unit-3 - [ DM ]
☞ Data Mining MCQs - Unit-4 - [ DM ]
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