Association Rule Mining Basics Quiz

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2. What is a common application of association rule mining in retail?

Predicting customer lifetime value

Product placement and cross-selling recommendations

Classifying customers into segments

Forecasting monthly sales revenue

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4. In association rule mining, what does 'conviction' measure?

The ratio of expected to observed frequency of the rule

How much the rule contradicts the data

The implication strength if A occurs then B must occur

The percentage of correct predictions

5. True or False: Confidence and support are independent measures of rule quality.

True

False

6. Which algorithm is an improvement over Apriori that reduces candidate generation?

K-means clustering

Eclat

Hierarchical clustering

Decision trees

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8. What is the relationship between support and computational complexity in the Apriori algorithm?

Higher support threshold requires examining more itemsets

Lower support threshold requires examining more itemsets

Support threshold does not affect computational cost

Computational complexity decreases exponentially with support

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10. True or False: A rule with 100% confidence always represents a strong association in the data.

True

False

11. Which metric is best used to identify truly interesting association rules, accounting for both support and confidence?

Support alone

Confidence alone

Lift

Transaction count

12. In association rule mining, what does 'support' measure?

The proportion of transactions containing both itemsets A and B

The strength of the relationship between two items

The frequency of a single itemset in the dataset

The percentage increase in probability when B is given A

13. In market basket analysis, what does a lift value of 1.5 for milk → bread suggest?

50% of customers buy milk and bread together

Buying milk makes bread 50% more likely to be purchased

Bread appears in 50% of all transactions

The rule has 1.5 times the expected frequency

14. Which statement about association rules is correct?

A rule with high support always has high confidence

Confidence and support measure different aspects of a rule

Lift is always equal to confidence divided by support

Rules are generated before itemsets are identified

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16. Which of the following is a characteristic of the Apriori algorithm?

It uses a bottom-up approach starting with frequent itemsets

It examines all possible itemsets simultaneously

It requires setting a maximum itemset size upfront

It randomly samples transactions for efficiency

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18. A rule A → B has confidence of 0.8. What does this indicate?

80% of all transactions contain both A and B

80% of transactions with A also contain B

80% of transactions with B also contain A

The rule is statistically significant at 0.8 level

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20. What is the primary goal of the Apriori algorithm?

To classify transactions into predefined categories

To find frequent itemsets and generate association rules

To cluster similar items without supervision

To predict future transaction values

21. Which metric represents P(B|A) in association rule mining?

Support

Confidence

Lift

Leverage

Data+ Association Rule Mining Basics Quiz

1. The Apriori principle states that if an itemset is infrequent, all its ____ are also infrequent.

2.

What first name or nickname would you like us to use?

2. What is a common application of association rule mining in retail?

3. The ____ metric indicates the number of times more likely items A and B occur together than independently.

4. In association rule mining, what does 'conviction' measure?

5. True or False: Confidence and support are independent measures of rule quality.

6. Which algorithm is an improvement over Apriori that reduces candidate generation?

7. An association rule with support 0.05 and confidence 0.6 means the rule appears in 5% of transactions, and when the antecedent occurs, the consequent follows ____ of the time.

8. What is the relationship between support and computational complexity in the Apriori algorithm?

9. In the Apriori algorithm, candidate itemsets are generated from ____ itemsets in the previous iteration.

10. True or False: A rule with 100% confidence always represents a strong association in the data.

11. Which metric is best used to identify truly interesting association rules, accounting for both support and confidence?

12. In association rule mining, what does 'support' measure?

13. In market basket analysis, what does a lift value of 1.5 for milk → bread suggest?

14. Which statement about association rules is correct?

15. A minimum support threshold of 2% means only itemsets appearing in at least ____ of transactions are considered frequent.

16. Which of the following is a characteristic of the Apriori algorithm?

17. Lift greater than 1.0 indicates ____ between items.

18. A rule A → B has confidence of 0.8. What does this indicate?

19. If an itemset {A, B} has support of 0.15, this means ____ of all transactions contain both A and B.

20. What is the primary goal of the Apriori algorithm?

21. Which metric represents P(B|A) in association rule mining?