DATAWAREHOUSING AND DATA MINING ONLINE BITS

Code No: 05321203 Set No. 1
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
IV B.Tech. I Sem., II Mid-Term Examinations, Oct./Nov. – 2009
DATA WAREHOUSING & DATA MINING
Objective Exam
Name: ______________________________ Hall Ticket No.
Answer All Questions. All Questions Carry Equal Marks.Time: 20 Min. Marks: 20.
A
I. Choose the correct alternative:
1. Which step in Apriori algorithm uses apriori property? [ ]
(a) join step (b) prune step (c) candidate generation step (d) query processing
2. Which technique can be used to reduce the size of the candidate k-itemsets in Apriori algorithm?
[ ]
a) pruning (b) cleaning (c) hash based (d) partitioning
3. Classification belongs to which type of learning? [ ]
(a) supervised (b) unsupervised (c) rote (d) machine
4. What kind of attributes does log linear model require? [ ]
(a) categorical (b) continuous (c) binary (d) ordinal
5. Which among the following is a accuracy measure of classifier? [ ]
(a) support (b) confidence (c) specificity (d) recall
6. Which algorithm mines frequent itemsets without candidate generation? [ ]
a) apriori b) FP tree c) FP growth d) maxpattern
7. What kind of analysis is used to access the impact of an input variable on a network output?[ ]
(a) component analysis (b) sensitivity analysis (c) output analysis (d) jackknife analysis
8. Which of the following is a lazy learner for classification? [ ]
(a) nearest neighbor (b) back propagation
(c) decision tree induction (d) genetic algorithm
9. Which is not an accuracy measure for classifier? [ ]
(a) sensitivity (b) precision (c) specificity (d) recall
10. In a term frequency matrix, what does each column represent? [ ]
a) term b) document vector c) number of term occurences d) words
Cont…2
Code No: 05321203 -2- Set No. 1
II. Fill in the blanks:
11. Association rules are considered interesting if they satisfy both______ & ______ thresholds.
12. If a rule concerns associations between the presence or absence of items, it is ______ rule.
13. Apriori can be applied to improve the efficiency of answering ____ queries.
14. FP growth algorithm adopts_________ strategy of algorithm design process.
15. ______ variables are continuous measurements of a roughly linear scale.
16. A typical example of association rule mining is _______.
17. _________ is a frequent patterns whose proper super pattern is not frequent.
18. Manhattan distance is defined as d(i, j) = _________.
19. An interesting mining optimization method can be adopted in spatial association analysis. This method is called as____________.
20. _________ is the percentage of retrieved documents that are infact relevant to the query.
-oOo-
Code No: 05321203 Set No. 2
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
IV B.Tech. I Sem., II Mid-Term Examinations, Oct./Nov. – 2009
DATA WAREHOUSING & DATA MINING
Objective Exam
Name: ______________________________ Hall Ticket No.
Answer All Questions. All Questions Carry Equal Marks.Time: 20 Min. Marks: 20.
A
I. Choose the correct alternative:
1. What kind of attributes does log linear model require? [ ]
(a) categorical (b) continuous (c) binary (d) ordinal
2. Which among the following is a accuracy measure of classifier? [ ]
(a) support (b) confidence (c) specificity (d) recall
3. Which algorithm mines frequent itemsets without candidate generation? [ ]
a) apriori b) FP tree c) FP growth d) maxpattern
4. What kind of analysis is used to access the impact of an input variable on a network output?[ ]
(a) component analysis (b) sensitivity analysis (c) output analysis (d) jackknife analysis
5. Which of the following is a lazy learner for classification? [ ]
(a) nearest neighbor (b) back propagation
(c) decision tree induction (d) genetic algorithm
6. Which is not an accuracy measure for classifier? [ ]
(a) sensitivity (b) precision (c) specificity (d) recall
7. In a term frequency matrix, what does each column represent? [ ]
a) term b) document vector c) number of term occurences d) words
8. Which step in Apriori algorithm uses apriori property? [ ]
(a) join step (b) prune step (c) candidate generation step (d) query processing
9. Which technique can be used to reduce the size of the candidate k-itemsets in Apriori algorithm?
[ ]
a) pruning (b) cleaning (c) hash based (d) partitioning
10. Classification belongs to which type of learning? [ ]
(a) supervised (b) unsupervised (c) rote (d) machine
Cont…2
Code No: 05321203 -2- Set No. 2
II. Fill in the blanks:
11. FP growth algorithm adopts_________ strategy of algorithm design process.
12. ______ variables are continuous measurements of a roughly linear scale.
13. A typical example of association rule mining is _______.
14. _________ is a frequent patterns whose proper super pattern is not frequent.
15. Manhattan distance is defined as d(i, j) = _________.
16. An interesting mining optimization method can be adopted in spatial association analysis. This method is called as____________.
17. _________ is the percentage of retrieved documents that are infact relevant to the query.
18. Association rules are considered interesting if they satisfy both______ & ______ thresholds.
19. If a rule concerns associations between the presence or absence of items, it is ______ rule.
20. Apriori can be applied to improve the efficiency of answering ____ queries.
-oOo-
Code No: 05321203 Set No. 3
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
IV B.Tech. I Sem., II Mid-Term Examinations, Oct./Nov. – 2009
DATA WAREHOUSING & DATA MINING
Objective Exam
Name: ______________________________ Hall Ticket No.
Answer All Questions. All Questions Carry Equal Marks.Time: 20 Min. Marks: 20.
A
I. Choose the correct alternative:
1. Which algorithm mines frequent itemsets without candidate generation? [ ]
a) apriori b) FP tree c) FP growth d) maxpattern
2. What kind of analysis is used to access the impact of an input variable on a network output?[ ]
(a) component analysis (b) sensitivity analysis (c) output analysis (d) jackknife analysis
3. Which of the following is a lazy learner for classification? [ ]
(a) nearest neighbor (b) back propagation
(c) decision tree induction (d) genetic algorithm
4. Which is not an accuracy measure for classifier? [ ]
(a) sensitivity (b) precision (c) specificity (d) recall
5. In a term frequency matrix, what does each column represent? [ ]
a) term b) document vector c) number of term occurences d) words
6. Which step in Apriori algorithm uses apriori property? [ ]
(a) join step (b) prune step (c) candidate generation step (d) query processing
7. Which technique can be used to reduce the size of the candidate k-itemsets in Apriori algorithm?
[ ]
a) pruning (b) cleaning (c) hash based (d) partitioning
8. Classification belongs to which type of learning? [ ]
(a) supervised (b) unsupervised (c) rote (d) machine
9. What kind of attributes does log linear model require? [ ]
(a) categorical (b) continuous (c) binary (d) ordinal
10. Which among the following is a accuracy measure of classifier? [ ]
(a) support (b) confidence (c) specificity (d) recall
Cont…2
Code No: 05321203 -2- Set No. 3
II. Fill in the blanks:
11. A typical example of association rule mining is _______.
12. _________ is a frequent patterns whose proper super pattern is not frequent.
13. Manhattan distance is defined as d(i, j) = _________.
14. An interesting mining optimization method can be adopted in spatial association analysis. This method is called as____________.
15. _________ is the percentage of retrieved documents that are infact relevant to the query.
16. Association rules are considered interesting if they satisfy both______ & ______ thresholds.
17. If a rule concerns associations between the presence or absence of items, it is ______ rule.
18. Apriori can be applied to improve the efficiency of answering ____ queries.
19. FP growth algorithm adopts_________ strategy of algorithm design process.
20. ______ variables are continuous measurements of a roughly linear scale.
-oOo-
Code No: 05321203 Set No. 4
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
IV B.Tech. I Sem., II Mid-Term Examinations, Oct./Nov. – 2009
DATA WAREHOUSING & DATA MINING
Objective Exam
Name: ______________________________ Hall Ticket No.
Answer All Questions. All Questions Carry Equal Marks.Time: 20 Min. Marks: 20.
A
I. Choose the correct alternative:
1. Which of the following is a lazy learner for classification? [ ]
(a) nearest neighbor (b) back propagation
(c) decision tree induction (d) genetic algorithm
2. Which is not an accuracy measure for classifier? [ ]
(a) sensitivity (b) precision (c) specificity (d) recall
3. In a term frequency matrix, what does each column represent? [ ]
a) term b) document vector c) number of term occurences d) words
4. Which step in Apriori algorithm uses apriori property? [ ]
(a) join step (b) prune step (c) candidate generation step (d) query processing
5. Which technique can be used to reduce the size of the candidate k-itemsets in Apriori algorithm?
[ ]
a) pruning (b) cleaning (c) hash based (d) partitioning
6. Classification belongs to which type of learning? [ ]
(a) supervised (b) unsupervised (c) rote (d) machine
7. What kind of attributes does log linear model require? [ ]
(a) categorical (b) continuous (c) binary (d) ordinal
8. Which among the following is a accuracy measure of classifier? [ ]
(a) support (b) confidence (c) specificity (d) recall
9. Which algorithm mines frequent itemsets without candidate generation? [ ]
a) apriori b) FP tree c) FP growth d) maxpattern
10. What kind of analysis is used to access the impact of an input variable on a network output?[ ]
(a) component analysis (b) sensitivity analysis (c) output analysis (d) jackknife analysis
Cont…2
Code No: 05321203 -2- Set No. 4
II. Fill in the blanks:
11. Manhattan distance is defined as d(i, j) = _________.
12. An interesting mining optimization method can be adopted in spatial association analysis. This method is called as____________.
13. _________ is the percentage of retrieved documents that are infact relevant to the query.
14. Association rules are considered interesting if they satisfy both______ & ______ thresholds.
15. If a rule concerns associations between the presence or absence of items, it is ______ rule.
16. Apriori can be applied to improve the efficiency of answering ____ queries.
17. FP growth algorithm adopts_________ strategy of algorithm design process.
18. ______ variables are continuous measurements of a roughly linear scale.
19. A typical example of association rule mining is _______.
20. _________ is a frequent patterns whose proper super pattern is not frequent.
-oOo-