Introduction to Computing MCQs with Answers
Practice important Introduction to Computing MCQs with answers and explanations.
Multiple Choice Questions
Q2321: Fill in the blank: Data mining techniques are closely associated with __________ and have origins in statistical analysis.
- A: computer science
- B: networking
- C: operational databases
- D: statistical analysis
View Answer
D
Q2322: What is the primary goal of cluster analysis in data mining?
- A: To discover classes
- B: To describe properties within classes
- C: To find links between data groups
- D: To identify outliers
View Answer
A
Q2323: Which form of data mining identifies data entries that do not comply with the norm?
- A: Association analysis
- B: Sequential pattern analysis
- C: Outlier analysis
- D: Cluster analysis
View Answer
C
Q2324: What is association analysis used to find in data mining?
- A: Links between data groups
- B: Trends over time
- C: Data entries that do not comply with the norm
- D: Properties of data items
View Answer
A
Q2325: What type of analysis might reveal that customers who buy potato chips also buy beer and soda?
- A: Sequential pattern analysis
- B: Outlier analysis
- C: Cluster analysis
- D: Association analysis
View Answer
D
Q2326: Which form of data mining focuses on identifying patterns of behavior over time?
- A: Outlier analysis
- B: Cluster analysis
- C: Sequential pattern analysis
- D: Association analysis
View Answer
C
Q2327: What does sequential pattern analysis often reveal in economic systems?
- A: Trends and patterns over time
- B: Data entries that deviate from the norm
- C: Class properties of data
- D: Links between different data groups
View Answer
A
Q2328: In data mining, what is the term used for presenting data in multiple perspectives?
- A: Data warehouse
- B: Data cube
- C: Data mining
- D: Data cluster
View Answer
B
Q2329: What kind of issues should be considered when evaluating data mining results?
- A: Ethical issues
- B: Technical issues
- C: Financial issues
- D: Time issues
View Answer
A
Q2330: How can data mining help in identifying potential credit card theft?
- A: By analyzing patterns of credit card use
- B: By clustering customers into age groups
- C: By identifying outliers in purchase patterns
- D: By linking purchases with other items
View Answer
C