Data Reduction MCQs

1. What is data reduction in data mining?

(A) Deleting irrelevant data from the dataset

(B) Reducing the size of the dataset while retaining its integrity

(C) Adding noise to the dataset

(D) Aggregating data from multiple sources

2. Why is data reduction important in data mining?

(A) To increase computational complexity

(B) To improve the quality of data visualization

(C) To handle missing values in the dataset

(D) To improve efficiency and effectiveness of data mining algorithms

3. Which of the following is a technique for data reduction that focuses on selecting a subset of relevant features?

(A) Feature selection

(B) Data normalization

(C) Data sampling

(D) Data transformation

4. What is sampling in the context of data reduction?

(A) Converting continuous data values into categorical values

(B) Selecting a representative subset of data points from a larger dataset

(C) Adding variability to the dataset

(D) Removing duplicate records from the dataset

5. Which technique involves compressing the dataset to reduce storage space and processing time?

(A) Data imputation

(B) Data transformation

(C) Data compression

(D) Data validation

6. What is dimensionality reduction?

(A) Reducing the number of variables or features in the dataset

(B) Removing outliers from the dataset

(C) Normalizing data values

(D) Adding noise to the dataset

7. Which of the following is a popular technique for dimensionality reduction?

(A) Principal Component Analysis (PCA)

(B) Data imputation

(C) Mean normalization

(D) Data encoding

8. What does feature extraction involve in data reduction?

(A) Selecting a subset of relevant features for analysis

(B) Reducing the size of the dataset

(C) Transforming data into a standard format

(D) Generating new features based on existing data

9. Which technique focuses on reducing data redundancy by identifying and merging duplicate records?

(A) Data deduplication

(B) Data imputation

(C) Data normalization

(D) Data sampling

10. How does data reduction contribute to improving data mining outcomes?

(A) By increasing the complexity of algorithms

(B) By reducing the quality of data visualization

(C) By improving efficiency and performance of data mining algorithms

(D) By adding noise to the dataset

11. Which approach involves summarizing data by creating smaller, more manageable representations?

(A) Data summarization

(B) Data anonymization

(C) Data standardization

(D) Data enrichment

12. What is the primary goal of data reduction techniques such as feature selection?

(A) To add variability to the dataset

(B) To simplify the dataset without losing important information

(C) To introduce noise into the dataset

(D) To handle missing values in the dataset

13. How does data reduction help in data preprocessing?

(A) By increasing the size of the dataset

(B) By automating data collection processes

(C) By reducing computational costs and storage requirements

(D) By ignoring outliers in the dataset

14. Which technique involves transforming and aggregating data to create new, more meaningful variables?

(A) Data summarization

(B) Data sampling

(C) Data transformation

(D) Data masking

15. What role does data reduction play in preparing data for machine learning algorithms?

(A) It complicates the analysis process

(B) It simplifies the data representation while preserving relevant information

(C) It introduces noise into the dataset

(D) It increases the number of features in the dataset