Unit 3: Data Mining and Reduction

On this page, you will find Most important and Mostly asked previous year questions in your b tech semester exam from unit 3 Data Mining and Reduction of the subject Data Warehouse and Mining. 


  1. What do you mean by data mining? Why it is important?
  2. Define KDD. Identify and describe the phases in the KDD process.
  3. Explain the each steps involved in data mining.
  4. Describe the basic architecture of data mining.
  5. Describe in brief the important steps of data mining and data mining functionality.
  6. Write the various issue related to data mining.
  7. What is outliers? How outlier analysis can be done?
  8. Write and describe the important type of difficulties in data mining process.
  9. List and explain the 5 primitive for specifying a data mining task.
  10. Describe the difference between the following approaches for the integration of data mining system with database or data warehouse systems : no coupling , loose coupling , semi tight coupling , tight coupling.
  11. What do you mean by data preprocessing?
  12. What is z-score normalization.
  13. What do you mean by data cleaning? Explain the important types of data cleaning.
  14. What is clustering and regression ? Explain with suitable example.
  15. Describe in brief the process of data integration and transformation.
  16. Write a note on data cube aggregation.
  17. What do you mean by data reduction? Write and explain its types.
  18. Explain attribute subset selection method for data reduction with examples.
  19. Explain Principal Component Analysis (PCA) in detail.
  20. Write a short note on dimensionally reduction.
  21. Explain histogram. The following data are a list of prices of commonly sold items at a company. The numbers have been sorted 1,1,5,5,5,8,8,10,10,15,15,15,20,20,20,20 . Make a histogram for price using singleton buckets.
  22. Write a note on dimensionality reduction.
  23. Write a note on numerosity reduction.
  24. Distinguish between dimensionality reduction and numerosity reduction.
  25. What is discretization ? Also write its techniques.
  26. What do you mean by data mining ? Differentiate between data mining technique and data mining strategy.
  27. Write a short note on social implications of data mining.
  28. Write a short note on data mining metrics.