Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10.

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1 Code No: 126VW Set No. 1 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech. III Year, II Sem., II Mid-Term Examinations, April-2018 DATA WAREHOUSING AND DATA MINING Objective Exam Name: Hall Ticket No. Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10. A I Choose the correct alternative: 1. The a priori frequent itemset discovery algorithm moves in the lattice. A) Upward. B) Downward. C) Breadth wise. D)both upward and downward. 2. If a set is a frequent set and no superset of this set is a frequent set, then it is called. A) Maximal frequent set. B) Border set. C) Lattice. D) Infrequent sets. 3. is an example for case based-learning. A) Decision trees. B) Neural networks. C) Genetic algorithm. D) K-nearest neighbor. 4. is an essential process where intelligent methods are applied to extract data patterns. A) Data warehousing B) Data mining C) Text mining D) Data selection 5. is the process of finding a model that describes and distinguishes data classes or concepts. A) Data Characterization B) Data Classification C) Data discrimination D) Data selection 6. Data mining can also applied to other forms such as A) Data streams B) Sequence data C) Text data D)All of the Mentioned 7. The algorithm can be applied in cleaning data. A) Search. B) Pattern recognition. C) Learning. D) Clustering. 8. Which of the following clustering analysis method uses multi resolution approach A) STUNT. B) OPTICS. C) CLIQUE. D) Wave Cluster. 9. Which type of following clustering computes augmented cluster ordering A) OPTICS. B) CLIQUE. C) STING. D) CLUSTER. 10. Pick out a k-medoid algorithm. A) DBSCAN. B) BIRCH. C) PAM. D) CURE. Cont 2

2 Code No: 126VW :2: Set No. 1 II Fill in the Blanks 11. A priori algorithm is otherwise called as 12. The first phase of A Priori algorithm is. 13. Bayesian Belief Networks specify 14. Naive Bayes classifiers are 15. Are the components that define a Bayesian Belief Network 16. Bayesian Belief Networks also known as 17. k-nearest neighbor is same as 18. The partition algorithm uses 19. DBSCAN stands for 20. The Hierarchical Clustering algorithm finally produced by -ooo-

3 Code No: 126VW Set No. 2 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech. III Year, II Sem., II Mid-Term Examinations, April-2018 DATA WAREHOUSING AND DATA MINING Objective Exam Name: Hall Ticket No. Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10. A I Choose the correct alternative: 1. is an essential process where intelligent methods are applied to extract data patterns. A) Data warehousing B) Data mining C) Text mining D) Data selection 2. is the process of finding a model that describes and distinguishes data classes or concepts. A) Data Characterization B) Data Classification C) Data discrimination D) Data selection 3. Data mining can also applied to other forms such as A) Data streams B) Sequence data C) Text data D)All of the Mentioned 4. The algorithm can be applied in cleaning data. A) Search. B) Pattern recognition. C) Learning. D) Clustering. 5. Which of the following clustering analysis method uses multi resolution approach A) STUNT. B) OPTICS. C) CLIQUE. D) Wave Cluster. 6. Which type of following clustering computes augmented cluster ordering A) OPTICS. B) CLIQUE. C) STING. D) CLUSTER. 7. Pick out a k-medoid algorithm. A) DBSCAN. B) BIRCH. C) PAM. D) CURE. 8. The a priori frequent itemset discovery algorithm moves in the lattice. A) Upward. B) Downward. C) Breadth wise. D)both upward and downward. 9. If a set is a frequent set and no superset of this set is a frequent set, then it is called. A) Maximal frequent set. B) Border set. C) Lattice. D) Infrequent sets. 10. is an example for case based-learning. A) Decision trees. B) Neural networks. C) Genetic algorithm. D) K-nearest neighbor. Cont 2

4 Code No: 126VW :2: Set No. 2 II Fill in the Blanks 11. Naive Bayes classifiers are 12. Are the components that define a Bayesian Belief Network 13. Bayesian Belief Networks also known as 14. k-nearest neighbor is same as 15. The partition algorithm uses 16. DBSCAN stands for 17. The Hierarchical Clustering algorithm finally produced by 18. A priori algorithm is otherwise called as 19. The first phase of A Priori algorithm is. 20. Bayesian Belief Networks specify -ooo-

5 Code No: 126VW Set No. 3 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech. III Year, II Sem., II Mid-Term Examinations, April-2018 DATA WAREHOUSING AND DATA MINING Objective Exam Name: Hall Ticket No. Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10. A I Choose the correct alternative: 1. Data mining can also applied to other forms such as A) Data streams B) Sequence data C) Text data D)All of the Mentioned 2. The algorithm can be applied in cleaning data. A) Search. B) Pattern recognition. C) Learning. D) Clustering. 3. Which of the following clustering analysis method uses multi resolution approach A) STUNT. B) OPTICS. C) CLIQUE. D) Wave Cluster. 4. Which type of following clustering computes augmented cluster ordering A) OPTICS. B) CLIQUE. C) STING. D) CLUSTER. 5. Pick out a k-medoid algorithm. A) DBSCAN. B) BIRCH. C) PAM. D) CURE. 6. The a priori frequent itemset discovery algorithm moves in the lattice. A) Upward. B) Downward. C) Breadth wise. D)both upward and downward. 7. If a set is a frequent set and no superset of this set is a frequent set, then it is called. A) Maximal frequent set. B) Border set. C) Lattice. D) Infrequent sets. 8. is an example for case based-learning. A) Decision trees. B) Neural networks. C) Genetic algorithm. D) K-nearest neighbor. 9. is an essential process where intelligent methods are applied to extract data patterns. A) Data warehousing B) Data mining C) Text mining D) Data selection 10. is the process of finding a model that describes and distinguishes data classes or concepts. A) Data Characterization B) Data Classification C) Data discrimination D) Data selection Cont 2

6 Code No: 126VW :2: Set No. 3 II Fill in the Blanks 11. Bayesian Belief Networks also known as 12. k-nearest neighbor is same as 13. The partition algorithm uses 14. DBSCAN stands for 15. The Hierarchical Clustering algorithm finally produced by 16. A priori algorithm is otherwise called as 17. The first phase of A Priori algorithm is. 18. Bayesian Belief Networks specify 19. Naive Bayes classifiers are 20. Are the components that define a Bayesian Belief Network -ooo-

7 Code No: 126VW Set No. 4 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech. III Year, II Sem., II Mid-Term Examinations, April-2018 DATA WAREHOUSING AND DATA MINING Objective Exam Name: Hall Ticket No. Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10. A I Choose the correct alternative: 1. Which of the following clustering analysis method uses multi resolution approach A) STUNT. B) OPTICS. C) CLIQUE. D) Wave Cluster. 2. Which type of following clustering computes augmented cluster ordering A) OPTICS. B) CLIQUE. C) STING. D) CLUSTER. 3. Pick out a k-medoid algorithm. A) DBSCAN. B) BIRCH. C) PAM. D) CURE. 4. The a priori frequent itemset discovery algorithm moves in the lattice. A) Upward. B) Downward. C) Breadth wise. D)both upward and downward. 5. If a set is a frequent set and no superset of this set is a frequent set, then it is called. A) Maximal frequent set. B) Border set. C) Lattice. D) Infrequent sets. 6. is an example for case based-learning. A) Decision trees. B) Neural networks. C) Genetic algorithm. D) K-nearest neighbor. 7. is an essential process where intelligent methods are applied to extract data patterns. A) Data warehousing B) Data mining C) Text mining D) Data selection 8. is the process of finding a model that describes and distinguishes data classes or concepts. A) Data Characterization B) Data Classification C) Data discrimination D) Data selection 9. Data mining can also applied to other forms such as A) Data streams B) Sequence data C) Text data D)All of the Mentioned 10. The algorithm can be applied in cleaning data. A) Search. B) Pattern recognition. C) Learning. D) Clustering. Cont 2

8 Code No: 126VW :2: Set No. 4 II Fill in the Blanks 11. The partition algorithm uses 12. DBSCAN stands for 13. The Hierarchical Clustering algorithm finally produced by 14. A priori algorithm is otherwise called as 15. The first phase of A Priori algorithm is. 16. Bayesian Belief Networks specify 17. Naive Bayes classifiers are 18. Are the components that define a Bayesian Belief Network 19. Bayesian Belief Networks also known as 20. k-nearest neighbor is same as -ooo-

Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10.

Answer All Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10. Code No: 126VR Set No. 1 JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech. III Year, II Sem., II Mid-Term Examinations, April-2018 SOFTWARE TESTING METHODOLOGIES Objective Exam Name: Hall Ticket