Q.no 21. What is another name of data matrix?
A : Single mode
B : Two mode
C : Multi mode
D : Large mode
Q.no 22. Which of the following is a predictive model?
A : Clustering
B : Regression
C : Summarization
D : Association rules
Q.no 23. The rule is considered as intersting if
A : They satisfy both minimum support and minimum confidence threshold
B : They satisfy both maximum support and maximum confidence threshold
C : They satisfy maximum support and minimum confidence threshold
D : They satisfy minimum support and maximum confidence threshold
Q.no 24. Data independence means
A : Data is defined separately and not included in programs
B : Programs are not dependent on the physical attributes of the data
C : Programs are not dependent on the logiical attributes of the data
D : Programs are not dependent on the physical attributes as well as logical attributes
of the data
Q.no 25. 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
Q.no 26. If first object X and Y coordinates are 3 and 5 respectively and second
object X and Y coordinates are 10 and 3 respectively, then what is Manhattan
disstance between these two objects?
A : 8
B : 13
C : 9
D : 10
Q.no 27. Number of records are comparatively more in
A : OLAP
B : OLTP
C : Same in OLAP and OLTP
D : Can not compare
Q.no 28. Which of the following operations are used to calculate proximity measures for ordinal attribute?
A : Replacement and discretization
B : Replacement and characterizarion
C : Replacement and normalization
D : Normalization and discretization
Q.no 29. Which of the following is necessary operation to calculate dissimilarity
between ordinal attributes?
A : Replacement of ordinal categories
B : Correlation coefficient
C : Discretization
D : Randomization
Q.no 30. Multilevel association rule mining is
A : Association rules generated from candidate-generation method
B : Association rules generated from without candidate-generation method
C : Association rules generated from mining data at multiple abstarction level
D : Assocation rules generated from frequent itemsets
Answer for Question No 21. is b
Answer for Question No 22. is b
Answer for Question No 23. is a
Answer for Question No 24. is d
Answer for Question No 25. is c
Answer for Question No 26. is c
Answer for Question No 27. is b
Answer for Question No 28. is c
Answer for Question No 29. is a
Answer for Question No 30. is c
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