Association rule mining MCQs January 8, 2026November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the main goal of association rule mining? (A) To predict continuous values (B) To cluster data into groups (C) To find hidden patterns and relationships in a dataset (D) To reduce the dimensionality of the data 2. In association rule mining, what does an itemset refer to? (A) A subset of transactions containing at least one item (B) A single item in the dataset (C) A rule that describes the relationship between items (D) A set of items that always appear together in the dataset 3. Which of the following is a measure of how frequently an itemset appears in the dataset? (A) Confidence (B) Lift (C) Support (D) Frequency 4. What is the confidence of an association rule A→B? (A) The probability of B occurring given that A has occurred (B) The proportion of transactions that contain A (C) The proportion of transactions that contain both A and B (D) The ratio of the frequency of A to the frequency of B 5. Which of the following measures indicates how much more likely the rule is to occur compared to random chance? (A) Confidence (B) Support (C) Frequency (D) Lift 6. What does the Apriori algorithm use to reduce the search space in association rule mining? (A) It uses random sampling to reduce computation (B) It combines itemsets into new ones (C) It eliminates itemsets that do not meet the minimum support (D) It generates frequent itemsets using clustering 7. In association rule mining, what is the significance of support? (A) It indicates how strong the relationship is between items (B) It measures the number of unique items in the dataset (C) It measures the correlation between items (D) It tells how frequently the rule is applicable to a transaction 8. What is the typical use of association rule mining in market basket analysis? (A) To predict the future sales of items (B) To segment customers into distinct groups (C) To identify associations between products bought together (D) To predict the next item a customer will buy 9. Which of the following is NOT a valid measure for evaluating the quality of an association rule? (A) Support (B) Confidence (C) Lift (D) Precision 10. What does Lift measure in association rule mining? (A) The likelihood of finding itemset B when itemset A occurs (B) The strength of the rule between two items (C) The relative increase in probability of B occurring given A (D) The increase in the overall frequency of items 11. Which of the following statements is true regarding association rules? (A) Association rules cannot be used for predicting numerical values (B) The rule A→B implies that A is bought after B (C) Rules with high confidence always have high support (D) They are typically used for clustering similar data points together 12. In the context of the Apriori algorithm, what does pruning refer to? (A) Removing irrelevant items from the dataset (B) Eliminating redundant rules (C) Reducing the size of the dataset (D) Removing itemsets that are unlikely to be frequent 13. What is the minimum support threshold used for in association rule mining? (A) It defines the maximum number of rules to be generated (B) It filters out rules that do not meet the desired frequency level (C) It helps in identifying the most confident rules (D) It determines the minimum correlation between items 14. Which of the following is a limitation of the Apriori algorithm? (A) It generates a large number of candidate itemsets (B) It requires less memory for large datasets (C) It is particularly efficient for categorical data (D) It uses a greedy approach to generate rules 15. What type of data does association rule mining typically work with? (A) Structured data with continuous values (B) Time series data (C) Sequential data with ordered elements (D) Transactional data or data in the form of a market basket