Data Reduction MCQs

By: Prof. Dr. Fazal Rehman | Last updated: August 6, 2024

Question: 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
Answer: B) Reducing the size of the dataset while retaining its integrity

Question: 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
Answer: D) To improve efficiency and effectiveness of data mining algorithms

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

A) Data transformation
B) Data normalization
C) Data sampling
D) Feature selection
Answer: D) Feature selection

Question: 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
Answer: B) Selecting a representative subset of data points from a larger dataset

Question: 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
Answer: C) Data compression

Question: What is dimensionality reduction?

A) Removing outliers from the dataset
B) Reducing the number of variables or features in the dataset
C) Normalizing data values
D) Adding noise to the dataset
Answer: B) Reducing the number of variables or features in the dataset

Question: 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
Answer: A) Principal Component Analysis (PCA)

Question: 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
Answer: D) Generating new features based on existing data

Question: 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
Answer: A) Data deduplication

Question: 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
Answer: C) By improving efficiency and performance of data mining algorithms

Question: Which approach involves summarizing data by creating smaller, more manageable representations?

A) Data summarization
B) Data anonymization
C) Data standardization
D) Data enrichment
Answer: A) Data summarization

Question: 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
Answer: B) To simplify the dataset without losing important information

Question: 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
Answer: C) By reducing computational costs and storage requirements

Question: 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
Answer: A) Data summarization

Question: 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
Answer: B) It simplifies the data representation while preserving relevant information

More Next Data Mining MCQs

1. Repeated Data Mining MCQs
2. Classification in Data mining MCQs
3. Clustering in Data mining MCQs
4. Data Analysis and Experimental Design MCQs
5. Basics of Data Science MCQs
6. Big Data MCQs
7. Caret Data Science MCQs 
8. Binary and Count Outcomes MCQs
9. CLI and Git Workflow

 

1. Data Preprocessing MCQs
   • Data Cleaning MCQs
   • Data Integration MCQs
   • Data Transformation MCQs
   • Data Reduction MCQs
2. Data Warehousing and OLAP MCQs
   • Data Warehouse Architecture MCQs
   • Online Analytical Processing (OLAP) MCQs
   • Data Cube Technology MCQs
3. Association Rule Learning MCQs
   • Apriori Algorithm MCQs
   • Eclat Algorithm MCQs
   • FP-Growth Algorithm MCQs
   • Market Basket Analysis MCQs
4. Classification
   • Decision Trees MCQs
   • Naive Bayes MCQs
   • k-Nearest Neighbors (k-NN) MCQs
   • Support Vector Machines (SVM) MCQs
   • Neural Networks MCQs
5. Clustering
   • k-Means Clustering MCQs
   • Hierarchical Clustering MCQs
   • DBSCAN MCQs
   • Mean Shift MCQs
6. Regression MCQs
7. Anomaly Detection MCQs
8. Text Mining and Natural Language Processing (NLP) MCQs
   • Text Preprocessing MCQs
   • Sentiment Analysis MCQs
9. Web Mining MCQs
   • Web Content Mining MCQs
   • Web Structure Mining MCQs
   • Web Usage Mining MCQs
10. Sequential Pattern Mining MCQs
11. Time Series Analysis MCQs

Data Mining Algorithms and Techniques MCQs

13. Frequent Itemset Mining MCQs
    • Apriori Algorithm MCQs
    • FP-Growth Algorithm MCQs
    • Eclat Algorithm MCQs
14. Dimensionality Reduction MCQs
15. Ensemble Methods MCQs
16. Data Mining Tools and Software MCQs
    • WEKA MCQs
    • RapidMiner MCQs
    • KNIME MCQs
    • Orange MCQs
17. Python  Programming for Data Mining MCQs (Pandas, NumPy, Scikit-Learn)
18. R Programming for Data Mining(dplyr, ggplot2, caret) MCQs
19. SQL Programming for Data Mining for Data Mining MCQs
20. Big Data Technologies MCQs
    • Hadoop and MapReduce  MCQs
    • Spark MCQs
    • NoSQL Databases (e.g., MongoDB, Cassandra) MCQs