Subject Code: 2170715
BE- SEMESTER-VII (NEW) EXAMINATION — WINTER 2020
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Subject Name: Data Mining and Business Intelligence
Time: 10:30 AM TO 12:30 PM
Total Marks: 56
Instructions:
- Attempt any FOUR questions out of EIGHT questions.
- Make suitable assumptions wherever necessary.
- Figures to the right indicate full marks.
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Q.No. | Question | Marks |
---|---|---|
Q.1 | (a) Differentiate OLAP and OLTP. | 03 |
(b) Define Schema. Explain the following schemas with suitable example. 1) Star 2) Snowflakes 3) Constellations | 04 | |
(c) Define noise data. Enlist the reasons for the presence of noise in data collection. Explain the methods to deal with noise. | 07 | |
Q.2 | (a) Define market basket analysis. Explain support and confidence with suitable example for finding the rules. | 03 |
(b) Explain the following terms. 1) Bias 2) Variance 3) Generalization 4) Outlier | 04 | |
(c) Define the Apriori Property. Generate candidate itemsets, frequent itemsets and association rules using Apriori algorithm on the following data set with minimum support count is 2 and minimum confidence is 60%. TID Items --- Content provided by FirstRanker.com --- T1 | BREAD, BUTTER, TOASTT2 | BUTTER, MILK T3 | BUTTER, BUSCUIT T4 | BREAD, BUTTER; MILK T5 | BREAD, BUSCUIT --- Content provided by FirstRanker.com --- T6 | BUTTER, BUSCUITT7 | BREAD;BUSCUIT T8 | BREAD, BUTTER, BUSCUIT, TOAST T9 | BREAD, BUTTER, BUSCUIT | 07 | |
Q.3 | (a) Minimum salary is 20,000/- Rs and Maximum salary is 1,70,000/- Rs. Map the salary 1,00,000/- Rs in new Range of (60,000 , 2,60,000) Rs using min-max normalization method. | 03 |
(b) Define time series database. Explain how to characterize time series data using trend analysis. | 04 | |
(c) Define data cube and explain any 3 operations on it. | 07 | |
Q.4 | (a) If Mean salary is 54,000Rs and standard deviation is 16,000 Rs then find z score value of 73,600 Rs salary. | 03 |
(b) Briefly explain linear and non-linear regression. | 04 | |
(c) Define Map and Reduce operations with suitable example. | 07 | |
Q.5 | (a) Discuss the following terms: 1) Tree Pruning 2) Information Gain 3) Spatiotemporal Databases | 03 |
(b) Discuss the major issues/challenges in data mining. | 04 | |
(c) Enlist the steps of K-Mean clustering algorithm. Explain it with suitable example. | 07 | |
Q.6 | (a) Discuss the following terms: 1) Correlation analysis 2) Gain Ration 3) Sequence Databases | 03 |
(b) Differentiate classification and prediction. | 04 | |
(c) Enlist the steps of ID3 decision tree generation algorithm. Explain it with suitable example and generate the tree. | 07 | |
Q.7 | (a) Explain Web structure and Web usage mining. | 03 |
(b) Draw and explain the topology of a multilayer, feed-forward Neural Network. | 04 | |
(c) Define big data and big data analytics. Explain big data distributed file system. | 07 | |
Q.8 | (a) Draw and explain Hadoop architecture. | 03 |
(b) Discuss Hash-based technique to improve efficiency of Apriori algorithm. | 04 | |
(c) Explain Baye’s Theorem and Naive Bayesian Classification with suitable example. | 07 |
Date: 28/01/2021
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