FP-growth algorithm MCQs December 22, 2025November 18, 2024 by u930973931_answers 15 min Score: 0 Attempted: 0/15 Subscribe 1. What does the FP in FP-Growth stand for? (A) Frequent Processing (B) Frequent Pairs (C) Frequent Patterns (D) Fast Patterns 2. Which of the following is a primary advantage of the FP-Growth algorithm over the Apriori algorithm? (A) It avoids candidate generation. (B) It generates association rules directly. (C) It does not require a minimum support threshold. (D) It works with continuous data without preprocessing. 3. What is the main data structure used in the FP-Growth algorithm? (A) Hash Table (B) Adjacency Matrix (C) Decision Tree (D) FP-Tree (Frequent Pattern Tree) 4. How is the FP-Tree constructed in the FP-Growth algorithm? (A) By arranging items in each transaction based on their frequency. (B) By recursively splitting the dataset into smaller subsets. (C) By pruning infrequent itemsets from the dataset. (D) By clustering similar transactions together. 5. What is the main computational step in the FP-Growth algorithm after constructing the FP-Tree? (A) Splitting the tree into smaller sub-trees. (B) Generating frequent itemsets through recursive pattern growth. (C) Sorting transactions by support values. (D) Creating candidate itemsets for every transaction. 6. What happens to items in the dataset that do not meet the minimum support threshold in FP-Growth? (A) They are included in the FP-Tree but marked as infrequent. (B) They are considered for the next iteration. (C) They are used to generate conditional trees. (D) They are ignored and not added to the FP-Tree. 7. How does FP-Growth handle large datasets efficiently? (A) By sampling a subset of the dataset. (B) By representing the data compactly in an FP-Tree. (C) By dividing the dataset into smaller clusters. (D) By parallelizing the candidate generation process. 8. Which of the following is a limitation of the FP-Growth algorithm? (A) It is computationally expensive due to candidate generation. (B) It cannot handle categorical data. (C) It requires a significant amount of memory for large datasets. (D) It only works for small datasets. 9. What is a conditional FP-Tree in the FP-Growth algorithm? (A) A tree built by combining all transactions. (B) A tree that contains only frequent items. (C) A tree generated for transactions containing a specific item. (D) A tree used for clustering transactions. 10. Which step is unnecessary in FP-Growth compared to the Apriori algorithm? (A) Generating frequent itemsets. (B) Setting a minimum support threshold. (C) Calculating the support for itemsets. (D) Generating candidate itemsets. 11. What type of dataset is the FP-Growth algorithm best suited for? (A) Datasets with high-dimensional continuous attributes. (B) Datasets with a large number of infrequent items. (C) Small datasets with few transactions. (D) Sparse datasets with many frequent patterns. 12. Which of the following is an application of the FP-Growth algorithm? (A) Predicting time-series trends. (B) Market basket analysis. (C) Dimensionality reduction. (D) Anomaly detection. 13. How is recursion used in the FP-Growth algorithm? (A) To build the FP-Tree from transactions. (B) To mine patterns from conditional FP-Trees. (C) To generate candidate itemsets for pruning. (D) To sort items based on frequency. 14. Which of the following is NOT an advantage of FP-Growth? (A) It reduces the computational cost by avoiding candidate generation. (B) It can handle datasets with continuous data directly. (C) It compresses the database into an FP-Tree. (D) It works efficiently with high-dimensional datasets. 15. In the FP-Growth algorithm, how is the ordering of items determined during FP-Tree construction? (A) Alphabetical order of item names. (B) Random order to minimize bias. (C) Ascending order of their support values. (D) Descending order of their frequency in the dataset.