Assignment 01
(Based on the content covered in the Chapter 1 Data Mining slides)
If you answer at least 75% of the questions correctly, you will receive full points for this
assignment.
Multiple-Choice Questions
1. What is the main purpose of data mining?
A. To store large amounts of data
B. To discover meaningful and useful patterns from data
C. To manually review every record
D. To replace database systems
2. Which of the following is a predictive data mining task?
A. Clustering
B. Association rule discovery
C. Classification
D. Sequential pattern discovery
3. Which of the following is a descriptive data mining task?
A. Classification
B. Regression
C. Clustering
D. Deviation detection
4. In classification, the attribute that we want to predict is called the:
A. Feature set
B. Class label
C. Cluster
D. Distance measure
5. What is the purpose of a test set in classification?
A. To train the model
B. To store data
C. To determine the accuracy of the model
D. To remove attributes
6. Which application is an example of classification?
A. Grouping similar documents
B. Predicting whether a credit-card transaction is fraudulent
C. Finding products frequently purchased together
D. Finding similar customers without labels
7. Customer churn classification attempts to predict whether a customer will:
A. Buy two products together
B. Move into another cluster
C. Be lost to a competitor
D. Increase product inventory
8. Clustering tries to create groups in which:
A. Objects within the same cluster are less similar
B. Objects within the same cluster are more similar
C. Every object has a class label
D. All clusters contain the same number of objects
9. For continuous attributes, which similarity/distance measure is mentioned in the slides?
A. Information gain
B. Euclidean distance
C. Confidence
D. Support
10. In good clustering, intracluster distances should generally be:
A. Maximized
B. Minimized
C. Equal to intercluster distances
D. Ignored
11. Which rule is an example of association rule discovery?
A. Age → Salary
B. {Diaper, Milk} → {Beer}
C. Customer → Cluster 2
D. Temperature → Wind Speed
12. Association rule discovery is commonly used to identify:
A. Items frequently purchased together
B. Future continuous values only
C. Fraudulent transactions only
D. Class labels
13. Sequential pattern discovery differs from ordinary association rules because it considers:
A. Only continuous variables
B. The order and timing of events
C. Only class labels
D. Euclidean distance
14. Regression is mainly used to predict:
A. A continuous-valued variable
B. A cluster number
C. A categorical class only
D. A sequence of purchased items only
15. Which is an example of regression from the slides?
A. Classifying a transaction as fraud or normal
B. Predicting sales based on advertising expenditure
C. Grouping customers by lifestyle
D. Discovering milk and beer relationships
16. Anomaly detection is designed to identify:
A. Normal behavior only
B. Significant deviations from normal behavior
C. Similar customer groups
D. Frequently purchased products
17. Which is an application of anomaly detection?
A. Credit-card fraud detection
B. Market segmentation
C. Document clustering
D. Product recommendation based only on association
18. Which of the following is NOT listed as a challenge of data mining?
A. Scalability
B. Data quality
C. Privacy preservation
D. Java compilation
True/False Questions
19. Classification normally uses labeled training data. True
20. Clustering requires every data point to already have a class label. False
21. Regression is used primarily to predict continuous values. True
22. Association rule discovery can be used for supermarket shelf management. True
23. Data mining is simply looking up a phone number in a phone directory. False
24. One reason for data mining is that human analysts may take weeks to discover useful
information in large datasets. True
Short-Answer Questions
25. Define data mining in your own words.
data mining is the process of processing large amounts of data to discover useful patterns,
relationships, and information
26. Explain the difference between predictive and descriptive data mining methods.
predictive data uses existing data to predict future outcomes while descriptive data
mining looks for patterns or relationships in existing data without necessarily predicting
an outcome
27. What is the difference between classification and clustering?
classification assigns data object to predefined classes
clustering groups objects based on their similarity without predefined class labels.
28. Give one real-world example of classification and identify the class label.
a bank could predict whether a loan applicant is high risk or low risk
29. Why is a test set needed when building a classification model?
a test set is used to evaluate how well the model performs on new unseen data. It helps
determine the models accuracy
30. Explain the meaning of the association rule {Diaper, Milk} → {Beer}.
the rule means that the customers who perchase diapers and mile will often purchase beer
along side it. It represents a relationship between products that frequently appear in the
same transaction
31. What is customer churn, and how can classification help a company reduce churn?
customer churn occurs when customers stop using a company’s service or leave for a
competitor. Classification helps predict whether a customer is likely to churn or stay.
32. Give two examples of regression applications.
predicting house prices based on size, location and other factors
predicting next month sales amount based on spent advertising
33. What is anomaly detection? Give one application.
anomaly detection identifies data point that are significantly different from normal
behavior an example of this is credit-card fraud detection can identify a transaction that is
very unusual
34. List four challenges associated with data mining.
some challenges are scalability, data quality, privacy preservation, and high-dimensional
data
Discussion / Exam-Style Questions
(Discuss these questions with your classmates and provide your answers.)
35. A telecommunications company wants to predict whether a customer will leave for a
competitor. Is this classification, clustering, association-rule discovery, or regression?
Explain your answer.
It is classification. The company is predicting one of two categories leave or stay. Since
the outcomes are predefined classes, this is a classification problem
36. A supermarket wants to discover which products are frequently purchased together.
Which data mining task should it use? Explain.
they should use association-rule discovery since the supermarket wants to find
relationships between the products.
37. A company has customer data but no predefined customer categories. It wants to divide
customers into similar groups for targeted marketing. Which technique should it use and
why?
it should use clustering since there are no predefined customer categories.
38. A company wants to predict next month's sales amount based on advertising spending.
Which data mining task is most appropriate? Explain.
they should choose regression since the sales amount is a continuous numerical value
which can be used to predict next months sales
39. Compare supervised learning and unsupervised learning using classification and
clustering as examples.
supervised learning uses labeled data. This model learns from examples where the correct
answer is already known. Classification is an example because it learns to predict
predefined class labels
unsupervised learning uses labeled data. This model tries to discover patterns or groups
on its own. Clustering is an example of
40. Explain the differences among classification, clustering, association-rule discovery,
regression, and anomaly detection. Give one application for each.
Classification predicts a predefined category. An example of this is fraud vs normal
transaction
Clustering groups similar object without labels. An example of customer segmentation
Association-rule discovery finds relationships between items or events. An example of
this is bread to butter
regression predicts a continuous numerical value. An example of this is predicting house
prices
Anomaly detection finds unusual or abnormal objects. An example of this is credit-card
fraud detection.