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
Answer: B) To find frequent itemsets and generate association rules
Explanation: The Apriori algorithm is designed to identify frequent itemsets in a dataset and use them to generate association rules.
2. The Apriori algorithm works based on which key property?
- A) Anti-monotone property
- B) Homogeneity property
- C) Convexity property
- D) Continuity property
Answer: A) Anti-monotone property
Explanation: The Apriori algorithm uses the anti-monotone property, which states that if an itemset is infrequent, all its supersets will also be infrequent.
3. What is the main computational challenge of the Apriori algorithm?
- A) Handling missing data
- B) High memory usage due to candidate generation
- C) Inability to generate association rules
- D) Poor accuracy with large datasets
Answer: B) High memory usage due to candidate generation
Explanation: The Apriori algorithm generates a large number of candidate itemsets, which can result in high memory usage and computational inefficiency.
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
Answer: D) Count the frequency of each individual item
Explanation: The algorithm begins by counting the frequency of each individual item to identify frequent 1-itemsets.
5. Which of the following metrics is NOT directly used in the Apriori algorithm?
- A) Support
- B) Confidence
- C) Lift
- D) Conviction
Answer: D) Conviction
Explanation: The Apriori algorithm primarily uses support to identify frequent itemsets and confidence to generate rules. Metrics like lift and conviction are typically used later for rule evaluation.
6. How does the Apriori algorithm handle infrequent itemsets?
- A) It prunes them from the candidate list.
- B) It combines them into larger itemsets.
- C) It assigns them a higher support value.
- D) It ignores them but keeps their supersets.
Answer: A) It prunes them from the candidate list.
Explanation: Infrequent itemsets and their supersets are pruned to reduce the search space.
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 eliminate infrequent itemsets from further consideration
- D) To calculate the confidence of a rule
Answer: C) To eliminate infrequent itemsets from further consideration
Explanation: The minimum support threshold helps in identifying and discarding itemsets that do not occur frequently enough.
8. What is the output of the Apriori algorithm after identifying frequent itemsets?
- A) Decision trees
- B) Regression coefficients
- C) Association rules
- D) Clusters
Answer: C) Association rules
Explanation: After finding frequent itemsets, the Apriori algorithm generates association rules that show relationships between items.
9. The Apriori algorithm generates candidate itemsets using which approach?
- A) Depth-first search
- B) Breadth-first search
- C) Join and prune
- D) Divide and conquer
Answer: C) Join and prune
Explanation: Candidate itemsets are generated by joining frequent itemsets from the previous iteration, and infrequent ones are pruned.
10. What is a frequent k-itemset in the Apriori algorithm?
- A) An itemset that occurs exactly kk times
- B) An itemset with kk items that satisfies the minimum support threshold
- C) An itemset that appears in all transactions
- D) An itemset that has high confidence but low support
Answer: B) An itemset with kk items that satisfies the minimum support threshold
Explanation: A frequent k-itemset is a set of kk items that occurs in the dataset with support greater than or equal to the minimum support threshold.
11. Which of the following is a limitation of the Apriori algorithm?
- A) It cannot be parallelized.
- B) It does not produce association rules.
- C) It is computationally expensive due to candidate generation and pruning.
- D) It requires labeled data for analysis.
Answer: C) It is computationally expensive due to candidate generation and pruning.
Explanation: The generation of candidate itemsets and pruning can make the Apriori algorithm computationally expensive, especially for large datasets.
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) They are discarded.
- D) Their support value is recalculated.
Answer: C) They are discarded.
Explanation: Infrequent itemsets are discarded to focus on the more significant ones.
13. Which algorithm is a commonly used alternative to the Apriori algorithm for mining frequent itemsets?
- A) K-Means
- B) FP-Growth
- C) DBSCAN
- D) SVM
Answer: B) FP-Growth
Explanation: FP-Growth is a more efficient alternative to Apriori, as it avoids candidate generation and uses an FP-tree data structure.
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 number of items in the dataset and the minimum support threshold
- D) The distance metric used
Answer: C) The number of items in the dataset and the minimum support threshold
Explanation: The algorithm’s complexity increases with the number of items and a low support threshold, as these lead to more candidate itemsets.
15. Why is the Apriori algorithm less efficient for large datasets?
- A) It does not support multi-core processing.
- B) It generates a large number of candidate itemsets.
- C) It only works with categorical data.
- D) It cannot generate association rules.
Answer: B) It generates a large number of candidate itemsets.
Explanation: The large number of candidate itemsets can make the algorithm inefficient for large datasets, both in terms of memory and computation time.