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Data Mining MCQs - Unit-2
1. Which algorithm is used for frequent itemset mining?
A . Decision tree algorithm
B . K-nearest neighbors algorithm
C . Apriori algorithm
D . Naive Bayes algorithm
2. 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
3. Which of the following is a data reduction technique?
A . Clustering
B . Classification
C . Sampling
D . Regression
4. 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
5. 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
6. Which of the following is not a measure of association used in association rule mining?
A . Support
B . Confidence
C . Lift
D . Entropy
7. Which algorithm requires multiple scans of data?
A . Apriori
B . FP Growth
C . Eclat
D . Decision Trees
8. 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
9. Which of the following is not a type of attribute used in data mining?
A . Nominal
B . Ordinal
C . Interval
D . Decimal
10.) A sequence of patterns that occur frequently is known as?
A . Frequent Item Set
B . Frequent Subsequence
C . Frequent Sub Structure
D . All of the above
11.) The analysis performed to uncover interesting statistical correlations between associated-attribute-value pairs is called?
A . Mining of Association
B . Mining of Clusters
C . Mining of Correlations
D . None of the above
12.) ______________________ 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
13.) Which of the following a valid multi-dimensional associaton rule?
A . buys (X,"Computer") => buys (X,"printer")
B . age (X,"20...29") ^ occupation (X,"student") => buys (X,"laptop")
C . buys (X,"Camera") ^ buys (X,"computer") => buys (X,"printer")
D . None of the above
14.) Which of the following is not a type of correlation?
A . Positive
B . Negative
C . Null
D . Zero
15.) 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
16. ____________________ is a popular form of background knowledge, which allows data to be mined at multiple levels.
17. The FP Growth algorithm is a popular method for frequent pattern mining in data mining. It works by constructing ________________
18. How do you calculate Confidence (A -> B)? ___________________________________
19. How do you calculate Lift {Bread -> Milk}? ____________________________________
20. Correlation Analysis is a data mining technique used to identify ___________________
21. The ________________ is not suitable for handling large datasets because it generates a large number of candidates.
22. The FP-tree (Frequent Pattern tree) is a data structure used in the FP Growth algorithm that stores the ______________ and _____________________.
23. Association rules that involve two or more dimensions or predicates can be referred to as ____________________________________.
24. Multilevel association rules can be mined efficiently using ____________________________.
25. Association rules that involve single dimension or predicate can be referred to as a ______________________ association rule.
26. SPM stands for ___________________________________________
27. Quantitative association rules having __________________ on the left-hand side and ______________________ on the right-hand side of the rule.
28. Steps in Apriori algorithm are ____________________ and __________________
29. If there is a pair of items, X and Y, which are frequently bought together then association rule is represented as _______________________.
30. Graph Patterm Mining uses __________________________ and __________________________ approachs to find relevant sub graphs.
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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