Download GTU BE/B.Tech 2019 Winter 7th Sem New 2170715 Data Mining And Business Intelligence Question Paper

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Subject Code: 2170715

GUJARAT TECHNOLOGICAL UNIVERSITY

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Instructions:

1. Attempt all questions.

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2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.

Q.1

1. Define data mining and list its features. [03]
2. Differentiate between OLTP and OLAP. [04]

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3. Describe the steps involved in data mining when viewed as a process of knowledge discovery. [07]

Q.2

1. Can BI be used for DM? Or vice versa? Justify. [03]
2. Explain in detail the extract/transform/load (ETL) design of an automated warehouse. [04]
3. Explain Mean, Median, Mode, Variance, Standard Deviation & five number summary with suitable database example. [07]

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OR

1. Is Graphical visualization better than text data? Justify your answer and explain different data visualization techniques. [07]

Q.3

1. Explain why data warehouses are needed for developing business solutions from today’s perspective. Discuss the role of data marts. [03]
2. Draw and Explain Snowflakes and Fact constellations Schema. [04]

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3. Define outlier analysis? Why outlier mining is important? Briefly describe the different approaches: statistical-based outlier detection, distance-based outlier detection and deviation-based outlier detection. [07]

OR

1. Discuss Following: (i) Meta Data/(ii) Virtual Warehouse [03]
2. Briefly outline the major steps of decision tree classification. Why is tree pruning useful in decision tree induction? [04]
3. In data pre-processing why do we need data smoothing? Discuss data smoothing by Binning. [07]

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Q.4

1. Draw the topology of a multilayer, feed-forward Neural Network. [03]
2. Describe Concept Hierarchy? List and briefly explain types of Concept Hierarchy. [04]
3. In real-world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem. [07]

OR

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1. Define “clustering”? Mention any two applications of clustering. [03]
2. Briefly explain Linear and Non-linear regression. [04]
3. Consider the following dataset and find frequent item sets and generate association rules for them using Apriori Algorithm. [07]

Date: 30/11/2019
Total Marks: 70

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T1 {I1,I2, I5}
T2 {I2,I4}
T3 {I2,I3}
T4 {I1,I2,I4}

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minimum support count is 2 minimum confidence is 60% .

Q.5

1. Differentiate Fact table vs. Dimension table [03]
2. What is market basket analysis? Explain the two measures of rule interestingness: support and confidence [04]
3. Briefly explain the life-cycle of Data Analytics and discuss the role of data scientists. [07]

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OR

1. Explain text mining using example. [03]
2. Explain data mining application for fraud detection. [04]
3. Discuss the main features of Hadoop Distributed File System. [07]

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