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Unit 2: MCQ Bank

A collection of 20 essential multiple-choice questions for Unit 2: Data Mining Introduction, complete with supporting statements for quick revision.

Questions and Answers​

1. What is Data Mining?​

  • Answer: B. Extracting hidden patterns from large data
  • Support: Data mining is the discovery of useful, non-trivial, and actionable patterns from vast datasets.

2. Data mining is also known as​

  • Answer: B. Knowledge extraction
  • Support: It is the core step in the Knowledge Discovery in Databases (KDD) process.

3. First step in KDD process​

  • Answer: C. Data cleaning
  • Support: This step removes noise and inconsistencies from raw data before further processing.

4. Step that combines multiple data sources​

  • Answer: B. Data integration
  • Support: Merges data from different sources (e.g., multiple databases or files) into a single, cohesive dataset.

5. Data relevant to analysis is selected in​

  • Answer: B. Data selection
  • Support: Only the data required for the specific mining task is chosen to minimize noise and improve performance.

6. Repository storing structured data with relationships​

  • Answer: B. Relational database
  • Support: Uses predefined schemas and tables with rows and columns to manage data with clear relationships.

7. Data warehouse is mainly used for​

  • Answer: B. Decision making
  • Support: Optimized for high-level analysis and decision support systems (DSS) rather than transactional processing.

8. Database containing time-based data​

  • Answer: B. Time-series database
  • Support: Stores data indexed by timestamps, allowing for the analysis of trends over time (e.g., stock market data).

9. Data mining functionalities are used to​

  • Answer: B. Identify patterns
  • Support: The fundamental goal of mining is to discover interesting and previously unknown patterns in data.

10. Descriptive data mining task​

  • Answer: C. Clustering
  • Support: Descriptive tasks group or characterize existing data (e.g., clustering similar customers together).

11. Classification belongs to​

  • Answer: B. Predictive task
  • Support: It predicts the class labels of new, unseen data based on a pre-trained model.

12. Clustering is​

  • Answer: B. Unsupervised learning
  • Support: Data is grouped based on similarity without any predefined class labels or training data.

13. Association rule mining is used for​

  • Answer: B. Market basket analysis
  • Support: Identifies strong relationships between items (e.g., customers who buy product A are likely to buy product B).

14. Regression is used for​

  • Answer: B. Prediction of numeric values
  • Support: Predicts continuous or numeric values (e.g., house prices, sales targets, or temperature).

15. Method representing knowledge using rules​

  • Answer: B. IF-THEN rules
  • Support: A simple way to represent mined knowledge (e.g., IF age > 18 THEN eligible_to_vote).

16. Decision tree consists of​

  • Answer: B. Nodes and branches
  • Support: Nodes represent decision points (attributes), and branches represent the outcomes of those decisions.

17. Outliers are​

  • Answer: C. Deviating data points
  • Support: These are data points that differ significantly from the general behavior or pattern of the dataset.

18. Which is NOT a data mining issue?​

  • Answer: C. Data mining
  • Support: "Data mining" is the field itself; issues include performance, scalability, diversity, and privacy.
  • Answer: B. Multimedia data mining
  • Support: Focuses on extracting patterns from non-textual data like images, audio, and video files.

20. Ethical issue in data mining relates to​

  • Answer: B. Privacy
  • Support: Concerns the potential misuse of personal or sensitive information during the mining and analysis process.