Data warehousing and Data Mining | B.E. Semester VII Summer 2018 | GTU Papers

Q: Q1. Explain: Star”, “Snowflake” and “Fact constellation” schemas of data warehouse.

This is question 1 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q1. Clearly state the differences between “Data Warehouses” and “Operational Database Systems”.

This is question 1 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q2. Define the term “Data Mining”. With the help of a suitable diagram explain the process of knowledge discovery from databases

This is question 2 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q2. What is data cleaning? Describe the different methods of handling missing values during data cleaning

This is question 2 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q2. What is data transformation? Explain the different data transformation approaches for transforming data.

This is question 2 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q3. What is Concept Hierarchy? List and explain different types of Concept Hierarchy.

This is question 3 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q3. What is an ‘outlier’? How do outliers impact the results of mining? Explain any one method to detect outliers

This is question 3 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q3. State the Apriori Property. Generate large itemsets and association rules using Apriori algorithm on the following data set having minimum support value as and minimum confidence value as 75%. TID Items Purchased T101 Cheese, milk, cookie T102 Butter, milk, bread T103 Cheese, butter, milk, bread T104 Butter, bread

This is question 3 (2 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q3. What is noise? Explain data smoothing methods as noise removal technique to divide given data into bins of size 3 by equal frequency bin partitioning, by bin means, by bin medians and by bin boundaries. Consider the data: 10, 2, 19, 18, 20, 18, 25, 28, 22

This is question 3 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Q: Q4. What is supervised and unsupervised learning? What is Cluster Analysis? List and explain requirements of clustering in data mining.

This is question 4 (7 marks) from the GTU Data warehousing and Data Mining BE Summer 2018 question paper. Subject code: 171601.

Questions17

Explain: Star”, “Snowflake” and “Fact constellation” schemas of data warehouse.

[7 marks]

Clearly state the differences between “Data Warehouses” and “Operational Database Systems”.

[7 marks]

Define the term “Data Mining”. With the help of a suitable diagram explain the process of knowledge discovery from databases

[7 marks]

What is data cleaning? Describe the different methods of handling missing values during data cleaning

[7 marks]

What is data transformation? Explain the different data transformation approaches for transforming data.

[7 marks]

What is Concept Hierarchy? List and explain different types of Concept Hierarchy.

[7 marks]

What is an ‘outlier’? How do outliers impact the results of mining? Explain any one method to detect outliers

[7 marks]

State the Apriori Property. Generate large itemsets and association rules using Apriori algorithm on the following data set having minimum support value as and minimum confidence value as 75%. TID Items Purchased T101 Cheese, milk, cookie T102 Butter, milk, bread T103 Cheese, butter, milk, bread T104 Butter, bread

[2 marks]

What is noise? Explain data smoothing methods as noise removal technique to divide given data into bins of size 3 by equal frequency bin partitioning, by bin means, by bin medians and by bin boundaries. Consider the data: 10, 2, 19, 18, 20, 18, 25, 28, 22

[7 marks]

What is supervised and unsupervised learning? What is Cluster Analysis? List and explain requirements of clustering in data mining.

[7 marks]

What are the methods to evaluate accuracy of classifier/predictor?

[7 marks]

Write steps of K-Means clustering algorithm with its pros and cons. How K- Mean clustering method differs from K-Medoid clustering method?

[7 marks]

State and explain attribute selection measure with suitable example.

[7 marks]

List and explain various OLAP operations.

[7 marks]

What is time series database? How to characterize the time series data using trend analysis?1

[7 marks]

What is web log? Explain web structure mining and web usage mining in detail.

[7 marks]

Discuss the characteristics and limitations of Neural Networks. Mention any two suitable applications of neural networks.

[7 marks]

Data warehousing and Data Mining — Summer 2018

Questions17

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