Printed Pages: 02
Paper ID: 110626
Sub Code: NCS 066
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Roll No.
B.Tech (SEM VI) THEORY EXAMINATION 2017-18
DATAWAREHOUSING AND DATA MINING
Time: 3 Hours
Total Marks: 100
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Note: 1. Attempt all Sections. If require any missing data; then choose suitably.
SECTION A
- Attempt all questions in brief. 2 x 10 = 20
- Draw the diagram for key steps of data mining.
- Define the term Support and Confidence.
- What are attribute selection measures? What is the drawback of information gain?
- Differentiate between classification and clustering
- Write the statement for Apriori Algorithm.
- What are the drawbacks of k-mean algorithm?
- What is Chi Square test?
- Compare Roll up, Drill down operation.
- What are Hierarchical methods for clustering?
- Name main features of Genetic Algorithm.
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SECTION B
- Attempt any three of the following: 10 x 3 = 30
- Explain the data mining / knowledge extraction process in detail?
- Differentiate between OLAP and OLTP.
- Find frequent patterns and the association rules by using Apriori Algorithm for the following transactional database:
boughtTID Items T100 M, O, N, K, E, Y T200 O, O, N, K, E, Y T300 M, A, K, E T400 M, U, C, K, Y T500 C, O, O, K, I, E
Let Minimum support = 60% and Minimum Confidence = 80% - What are different database schemas. Show with an example?
- How data back-up and data recovery is managed in data warehouse?
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SECTION C
- Attempt any one part of the following: 10 x 1 = 10
- Draw the 3-tier data warehouse architecture. Explain ETL process.
- Elaborate the different strategies for data cleaning.
- Attempt any one part of the following: 10 x 1 = 10
- What are different clustering methods? Explain STING in detail.
- What are the applications of data warehousing? Explain web mining and spatial mining.
- Attempt any one part of the following: 10 x 1 = 10
- Define data warehouse. What strategies should be taken care of while designing a warehouse?
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FirstRanker.com - Write short notes on the following:
- Concept Hierarchy
- ROLAP vs MOLAP
- Gain Ratio
- Classification Vs Clustering
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- Attempt any one part of the following: 10 x 1 = 10
- Compute the decision rules by deriving a decision tree classifier and information gain as selection measure for the given database in table.
Table 6--- Content provided by FirstRanker.com ---
Age Income Student Credit rating Class: buys computer youth high No Fair No youth high No Excellent No middle high No Fair Yes aged senior No Fair Yes senior low Yes Fair Yes senior low Yes Excellent No middle low Yes Excellent Yes aged youth No Fair No youth low Yes Fair Yes senior medium Yes Fair Yes youth medium Yes Excellent Yes middle medium No Excellent Yes aged high Yes Fair Yes aged medium No Excellent No
Given: Gain (age) = 0.246, Gain (student) = 0.151 and Gain (Credit Rating) = 0.048 - What is Laplacian Correction in Bayesian Classifier? Compute the class of the following tuple by using Bayesian classification for given database in table 6.
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X = (Age = senior, Credit rating = fair, Income = medium, student = no) - Attempt any one part of the following: 10 x 1 = 10
- Write the k-mean algorithm. Suppose that the data mining task is to cluster points (with (x,y) representing location ) into three clusters, where the points are:
A1 (2, 10), A2 (2, 5) A3 (8, 4)--- Content provided by FirstRanker.com ---
B1 (5, 8), B2 (7, 5) B3 (6, 4)
C1 (1, 2), C2 (4, 9)
The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only The three cluster centers after the first round of execution. - What is Hierarchical method for clustering? Explain BIRCH method.
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