Apriori algorithm MCQs December 22, 2025November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What is the main purpose of the Apriori algorithm? (A) To classify data into predefined categories (B) To find frequent itemsets and generate association rules (C) To predict future trends in time series data (D) To reduce the dimensionality of the dataset 2. The Apriori algorithm works based on which key property? (A) Homogeneity property (B) Anti-monotone property (C) Convexity property (D) Continuity property 3. What is the main computational challenge of the Apriori algorithm? (A) Handling missing data (B) Poor accuracy with large datasets (C) Inability to generate association rules (D) High memory usage due to candidate generation 4. What is the first step in the Apriori algorithm? (A) Generate frequent itemsets of size 2 (B) Prune infrequent itemsets (C) Generate all possible itemsets and calculate their support (D) Count the frequency of each individual item 5. Which of the following metrics is NOT directly used in the Apriori algorithm? (A) Support (B) Confidence (C) Lift (D) Conviction 6. How does the Apriori algorithm handle infrequent itemsets? (A) It combines them into larger itemsets. (B) It prunes them from the candidate list. (C) It assigns them a higher support value. (D) It ignores them but keeps their supersets. 7. What is the minimum support threshold used for in the Apriori algorithm? (A) To filter out frequent itemsets (B) To determine the frequency of the dataset (C) To calculate the confidence of a rule (D) To eliminate infrequent itemsets from further consideration 8. What is the output of the Apriori algorithm after identifying frequent itemsets? (A) Association rules (B) Regression coefficients (C) Decision trees (D) Clusters 9. The Apriori algorithm generates candidate itemsets using which approach? (A) Join and prune (B) Breadth-first search (C) Depth-first search (D) Divide and conquer 10. What is a frequent k-itemset in the Apriori algorithm? (A) An itemset that occurs exactly k times (B) An itemset that appears in all transactions (C) An itemset with k items that satisfies the minimum support threshold (D) An itemset that has high confidence but low support 11. Which of the following is a limitation of the Apriori algorithm? (A) It cannot be parallelized. (B) It is computationally expensive due to candidate generation and pruning. (C) It does not produce association rules. (D) It requires labeled data for analysis. 12. What happens to the candidate itemsets in the Apriori algorithm when their support is below the minimum support threshold? (A) They are split into smaller subsets. (B) They are merged with other itemsets. (C) Their support value is recalculated. (D) They are discarded. 13. Which algorithm is a commonly used alternative to the Apriori algorithm for mining frequent itemsets? (A) K-Means (B) SVM (C) DBSCAN (D) FP-Growth 14. What is the complexity of the Apriori algorithm largely dependent on? (A) The number of transactions in the dataset (B) The size of the largest frequent itemset (C) The distance metric used (D) The number of items in the dataset and the minimum support threshold 15. Why is the Apriori algorithm less efficient for large datasets? (A) It does not support multi-core processing. (B) It only works with categorical data. (C) It generates a large number of candidate itemsets. (D) It cannot generate association rules.