Here are the question papers of the Data Mining Examination of CTEVT in the 6th semester of the Diploma in Computer Engineering.
Here is the important question for Data mining which is mostly repeated also,
Course Introduction
Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. the course will cover all these issues and will illustrate the whole process by example
Data Mining question paper of 6th semester 2078/2079
1. Define Data Mining with its functionalities. Mention important applications of data mining.
2. Discuss data mining as a step in the knowledge discovery process and the various challenges associated with it.
3. Explain various data mining functionalities.
4. Describe the data warehouse with its characteristics. Differentiate between data warehouse and database.
5. Explain various data warehouse schemas with their advantages and disadvantages.
OR
Explain star schema, snowflake schema, and fact constellation schema with their advantages and disadvantages.
6. How does the example snowflake schema differ from a star schema? List any two advantages and disadvantages of the snowflakes schema.
7. What are the important characteristics of OLAP and OLTP. Differentiate OLAP with OLTP.
8. What do you understand by Slice and Dice, Drill up, a, and Drill-down in multidimensional data? Explain each of them in detail.
9. What do you understand by data preprocessing? Explain the terms data extraction, cleanup, and transformation.
10. What do you mean by data mining query language (DMQL)? Explain DMQL with syntax
and proper terms.
11. Define clustering. What are the different categories of clustering? Explain in brief.
12. Explain k-means clustering with its advantages and drawbacks.
13. Explain divisive clustering and agglomerative clustering in detail.
15. Explain the concept of Classification with an example. List out various classification
algorithms and explain any one of them.
16. Define the Decision Tree. Assume any information and draw a decision tree.
17. Explain entropy and information gain in detail with appropriate expressions.
18. Explain linear and non-linear regression with appropriate figures.
19. Explain the apriori algorithm with its important properties. the
20. Explain the FP-growth algorithm in detail.
21. Explain advanced data mining with its important features.
22. Explain Time Series data and its analysis in brief. List out the areas where time series
the analysis is used and explains any one of them in detail.
23. Explain information retrieval. How do you retrieve the image and video information? Explain.
24. Write Short notes on
– KDD
– DMQL
– Data Cube
– Multidimensional Data
– Data Cleaning Strategies
– Classification vs Clustering
– Advanced-Data Mining
– Application of data mining
– Support Vector Machine
– Deep learning
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