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S 23 DWM

This document outlines the examination details for the Data Warehousing and Mining subject at Gujarat Technological University for Summer 2023. It includes instructions for the exam, a breakdown of questions across five sections, and topics such as OLAP operations, regression comparison, data mining integration, and clustering methods. The total marks for the exam are 70, and students are required to attempt all questions.

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12 views1 page

S 23 DWM

This document outlines the examination details for the Data Warehousing and Mining subject at Gujarat Technological University for Summer 2023. It includes instructions for the exam, a breakdown of questions across five sections, and topics such as OLAP operations, regression comparison, data mining integration, and clustering methods. The total marks for the exam are 70, and students are required to attempt all questions.

Uploaded by

yugkp137
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Seat No.: ________ Enrolment No.

___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER–VI (NEW) EXAMINATION – SUMMER 2023
Subject Code:3161610 Date:14-07-2023
Subject Name:Data Warehousing and 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.
4. Simple and non-programmable scientific calculators are allowed.
MARKS
Q.1 (a) Explain various OLAP operations. 03
(b) Compare Linear and nonlinear regression. 04
(c) Explain Star, Snowflake and Fact constellation” schemas of data warehouse with 07
suitable example.
Q.2 (a) Define the following terms: 03
1. OLAP
2. OLTP
3. OLAM
(b) What is data mining integration in data warehousing? Explain with an example 04
(c) Discuss data discretization and concept hierarchy generation. 07
OR
(c) Explain Naïve Bayesian classification in detail with example. 07

Q.3 (a) Define techniques to improve the efficiency of Apriori algorithm. 03


(b) Define nominal and ordinal variables 04
(c) What is data transformation? Explain the different data transformation approaches for 07
transforming data.
OR
Q.3 (a) What is feature selection in data mining? 03
(b) Define Fact Table and dimension table. 04
(c) What is the confusion matrix, and how is it used to evaluate a classifier? 07

Q.4 (a) Define Support & Confidence. 03


(b) Discuss Issues regarding Classification and prediction 04
(c) Describe and explain the different types of clustering methods. 07
OR
(a) What is outlier? Discuss different methods for outlier detection. 03
(b) Explain the difference between a data warehouse and a data mart 04
(c) What are the reasons for the presence of ‘noise’ in data collected for mining? 07
Explain the methods to deal with noise.

Q.5 (a) Define data mart. 03


(b) What is association rule mining? Explain with an example. 04
(c) What is Decision Tree? Explain how classification is done using decision tree 07
induction.
OR
Q.5 (a) What is a data cube? 03
(b) Discuss the limitations and challenges of data mining 04
(c) Explain Web Mining in detail. 07

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