Download GTU BE/B.Tech 2018 Winter 7th Sem Old 171601 Data Warehousing And Data Mining Question Paper

Download GTU (Gujarat Technological University) BE/BTech (Bachelor of Engineering / Bachelor of Technology) 2018 Winter 7th Sem Old 171601 Data Warehousing And Data Mining Previous Question Paper

1
Seat No.: ________ Enrolment No.___________

GUJARAT TECHNOLOGICAL UNIVERSITY

BE - SEMESTER ?VII (OLD) EXAMINATION ? WINTER 2018
Subject Code: 171601 Date: 03/12/2018

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 (a) What is Data Mining? Explain Data mining as one step of Knowledge
Discovery Process.
07
(b) Differentiate between Operational Database System and Data Warehouse. 07

Q.2 (a) Explain Data Cleaning process for missing values and noisy data. 07
(b) Explain three-tier data warehouse architecture. 07
OR
(b) Explain mining in following Databases with example.
1. Temporal Databases
2. Sequence Databases
3. Spatial Databases and Saptiotemporal Databases.
07

Q.3 (a) What is Cuboid? Explain various OLAP Operations on Data Cube with
example.
07
(b) Explain Fp -Growth Algorithm for finding Frequent Item-sets. 07
OR
Q.3 (a) State the Apriori Property. Generate large itemsets and association rules using
Apriori algorithm on the following data set with minimum support value and
minimum confidence value set as 50% and 75% respectively.

TID Items Purchased
T101 Cheese, Milk, Cookies
T102 Butter, Milk, Bread
T103 Cheese, Butter, Milk, Bread
T104 Butter, Bread
07
(b) What is Concept Hierarchy? List and explain types of Concept Hierarchy. 07

Q.4 (a) Explain k-means and k-medoids algorithm of clustering. 07
(b) What is ?Information Gain?? Explain the steps required to generate a Decision
Tree from a training data set.
07
OR
Q.4 (a)
Explain Baye?s Theorm and Na?ve Bayesian Classification.
07
(b) Explain linear regression? What are the reasons for not using the linear
regression model to estimate the output data?
07

Q.5 (a) Discuss the application of data warehousing and data mining in government
sector.
07
(b) What are neural networks? Describe the various factors which make them
useful for classification and prediction in data mining. Explain how the
topology of neural network is designed.
07
OR

Q.5 (a) Explain different types of Web Mining with example. 07
(b) What are the challenges for effective resource and knowledge discovery in
mining the world wide web?
07
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