Exam Details
| Subject | data warehousing & data mining | |
| Paper | ||
| Exam / Course | mca | |
| Department | ||
| Organization | Gujarat Technological University | |
| Position | ||
| Exam Date | June, 2017 | |
| City, State | gujarat, ahmedabad |
Question Paper
1
Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA SEMESTER- IV• EXAMINATION SUMMER 2017
Subject Code: 2640005 Date:08/06/2017
Subject Name: Data Warehousing and Data Mining
Time: 10.30 AM TO 01.00 PM Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1
Do as directed
Define Multi feature Cube.
Explain 4 Key words of Data Warehouse.
Explain in brief Star, Snowflake and Galaxy schema with example.
02
02
03
Explain KDD process with its steps.
07
Q.2
What is the need for outlier analysis? Discuss various methods of Outlier detection
07
Discuss data cleaning techniques for missing values and Noisy data.
07
OR
Explain in detail Dimensionality reduction and Numerosity reduction.
07
Q.3
What is Apriori Property? Explain Apriori algorithm.
07
Explain the following:
Concept Hierarchy
ii) Self-exp, in-Exp, Path-exp
04
03
OR
Q.3
Discuss various techniques used for Improving the Efficiency of Apriori.
07
Explain the following:
Attribute Removal and Attribute Generalization
ii) Data Mart
04
03
Q.4
What is Data mining? Discuss major issues in data mining.
07
Explain in brief Boosting and Bagging.
07
OR
Q.4
Explain Decision tree algorithm with terms "Tree Induction" and "Tree Pruning".
07
Discuss Holdout, random subsampling, cross validating and the bootstrap techniques for evaluating the Accuracy of a Classifier or Predictor.
07
Q.5
Explain k-mean algorithm for clustering.
05
Describe application of Data Mining in Financial Data Analysis.
05
Discuss OLAP Servers.
04
OR
Q.5
Explain k-medoid algorithm for clustering.
05
Describe application of Data Mining in Intrusion detection.
05
Differentiate Between OLAP and OLTP.
04
Seat No.: Enrolment
GUJARAT TECHNOLOGICAL UNIVERSITY
MCA SEMESTER- IV• EXAMINATION SUMMER 2017
Subject Code: 2640005 Date:08/06/2017
Subject Name: Data Warehousing and Data Mining
Time: 10.30 AM TO 01.00 PM Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1
Do as directed
Define Multi feature Cube.
Explain 4 Key words of Data Warehouse.
Explain in brief Star, Snowflake and Galaxy schema with example.
02
02
03
Explain KDD process with its steps.
07
Q.2
What is the need for outlier analysis? Discuss various methods of Outlier detection
07
Discuss data cleaning techniques for missing values and Noisy data.
07
OR
Explain in detail Dimensionality reduction and Numerosity reduction.
07
Q.3
What is Apriori Property? Explain Apriori algorithm.
07
Explain the following:
Concept Hierarchy
ii) Self-exp, in-Exp, Path-exp
04
03
OR
Q.3
Discuss various techniques used for Improving the Efficiency of Apriori.
07
Explain the following:
Attribute Removal and Attribute Generalization
ii) Data Mart
04
03
Q.4
What is Data mining? Discuss major issues in data mining.
07
Explain in brief Boosting and Bagging.
07
OR
Q.4
Explain Decision tree algorithm with terms "Tree Induction" and "Tree Pruning".
07
Discuss Holdout, random subsampling, cross validating and the bootstrap techniques for evaluating the Accuracy of a Classifier or Predictor.
07
Q.5
Explain k-mean algorithm for clustering.
05
Describe application of Data Mining in Financial Data Analysis.
05
Discuss OLAP Servers.
04
OR
Q.5
Explain k-medoid algorithm for clustering.
05
Describe application of Data Mining in Intrusion detection.
05
Differentiate Between OLAP and OLTP.
04
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