Ch3 data minig

1. What is the primary purpose of summarizing low-level data to higher-level data abstraction?

• A) To make the data more complex

• B) To reduce mining time and discover stable patterns

• C) To increase the number of attributes

• D) To avoid data analysis

2. Which of the following techniques is used to summarize data into a higher level of abstraction?

• A) Applying complex machine learning algorithms

• B) Using a given concept hierarchy for generalization

• C) Increasing the number of attributes

• D) Adding more raw data

3. Why is rescaling data values important in the process of data abstraction?

• A) To reduce computation errors

• B) To make the data more difficult to interpret

• C) To discover more stable patterns and improve data mining efficiency

• D) To remove essential information from the data

4. Which aggregate function is typically used to summarize data in abstraction?

• A) Filter

• B) Count, sum, or average

• C) Index

• D) Search

5. What happens when certain attributes are dropped in data abstraction?

• A) It increases the complexity of the data

• B) It reduces the dataset size and simplifies the pattern discovery

• C) It introduces more attributes

• D) It makes data analysis impossible