Data Mining MCQ Multiple Choice Questions Answers | Quiz for Practice

Welcome to our Data Mining MCQs section! These multiple-choice questions are designed to help students and exam aspirants test their knowledge and prepare for exams, interviews, and more.

About Data Mining MCQ Questions

Data Mining MCQs cover a wide range of topics including data preprocessing, pattern discovery, clustering, classification, and more. These questions are crafted to ensure a comprehensive understanding of the subject, helping you grasp key concepts and techniques used in data mining.

Why Practice Data Mining Objective Questions?

Practicing Data Mining MCQs offers several benefits. They help you prepare for school and college exams, competitive exams, and job interviews. Regular practice enhances your problem-solving skills, reinforces your understanding of complex concepts, and builds your confidence in handling real-world data mining challenges.

Who Should Use These MCQs?

  • Students preparing for school or college exams
  • Competitive exam aspirants
  • Candidates preparing for interviews

Data Mining MCQ Questions for Practice

1. How much percentage of the interesting information can be obtained by using SQL.

2. The un-normalized relation containing all attributes that exist in database is

3. Enrichment means

4. The system that can be used without knowledge of internal operation

5. The distance between two points that is calculated using Pythagoras theorem is

6. Which of the following is closely related to statistical significance and transparency?

7. What is a creative activity that has to be performed repeatedly in order to get best results.

8. The decision support system is used only for

9. Deep knowledge can be found only by using

10. Foreign key constraints are also referred as

11. What are the two important qualities of good learning algorithm.

12. Metadata describes

13. What itself has become a production factor of importance.

14. The partition of overall data warehouse is

15. DSS stands for

16. Which one is an example for case based-learning.

17. Redundancy refers to the elements of a message that can be derived from other parts of

18. Data mining algorithms require

19. The next stage to data selection in KDD process

20. What is the first stage in genetic algorithm.

21. EIS stands for

22. A coding operation in which an attribute with cardinality n is replaced by n binary attributes is called as

23. What is one of the genetic operators that are used to recombine the population of genetic material.

24. A database containing volatile data used for daily operation of an organization is

25. The technique of learning by generalizing from examples is

26. Genetic algorithm was proposed by

27. Which is the technique which is used for discovering patterns in dataset at the beginning of data mining process.

28. A natural way to visualize the process of training a self-organizing map is called

29. K-nearest neighbor is one of the

30. The complexity of data mining algorithm is represented by

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