1. What does the FP in FP-Growth stand for?
- A) Frequent Patterns
- B) Frequent Pairs
- C) Frequent Processing
- D) Fast Patterns
Answer: A) Frequent Patterns
Explanation: FP-Growth stands for
Frequent Pattern Growth, as it is used to find frequent patterns in a dataset.
2. Which of the following is a primary advantage of the FP-Growth algorithm over the Apriori algorithm?
- A) It generates association rules directly.
- B) It avoids candidate generation.
- C) It does not require a minimum support threshold.
- D) It works with continuous data without preprocessing.
Answer: B) It avoids candidate generation.
Explanation: FP-Growth eliminates the need for candidate generation by using a compact data structure called the FP-tree.
3. What is the main data structure used in the FP-Growth algorithm?
- A) Hash Table
- B) FP-Tree (Frequent Pattern Tree)
- C) Decision Tree
- D) Adjacency Matrix
Answer: B) FP-Tree (Frequent Pattern Tree)
Explanation: The FP-Growth algorithm uses an FP-Tree to compactly store the database while preserving itemset relationships.
4. How is the FP-Tree constructed in the FP-Growth algorithm?
- A) By pruning infrequent itemsets from the dataset.
- B) By recursively splitting the dataset into smaller subsets.
- C) By arranging items in each transaction based on their frequency.
- D) By clustering similar transactions together.
Answer: C) By arranging items in each transaction based on their frequency.
Explanation: The FP-Tree is constructed by ordering items in each transaction according to their frequency in descending order.
5. What is the main computational step in the FP-Growth algorithm after constructing the FP-Tree?
- A) Generating frequent itemsets through recursive pattern growth.
- B) Splitting the tree into smaller sub-trees.
- C) Sorting transactions by support values.
- D) Creating candidate itemsets for every transaction.
Answer: A) Generating frequent itemsets through recursive pattern growth.
Explanation: After constructing the FP-Tree, the algorithm generates frequent itemsets by recursively exploring conditional FP-trees.
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 ignored and not added to the FP-Tree.
- C) They are used to generate conditional trees.
- D) They are considered for the next iteration.
Answer: B) They are ignored and not added to the FP-Tree.
Explanation: Items that do not meet the minimum support threshold are discarded to reduce the search space.
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.
Answer: B) By representing the data compactly in an FP-Tree.
Explanation: The FP-Tree compresses the dataset by grouping transactions with shared prefixes, making it efficient for large datasets.
8. Which of the following is a limitation of the FP-Growth algorithm?
- A) It is computationally expensive due to candidate generation.
- B) It requires a significant amount of memory for large datasets.
- C) It cannot handle categorical data.
- D) It only works for small datasets.
Answer: B) It requires a significant amount of memory for large datasets.
Explanation: The FP-Tree structure can become large for massive datasets, which might lead to memory issues.
9. What is a conditional FP-Tree in the FP-Growth algorithm?
- A) A tree generated for transactions containing a specific item.
- B) A tree that contains only frequent items.
- C) A tree built by combining all transactions.
- D) A tree used for clustering transactions.
Answer: A) A tree generated for transactions containing a specific item.
Explanation: Conditional FP-Trees are generated for each item to explore the patterns associated with that item.
10. Which step is unnecessary in FP-Growth compared to the Apriori algorithm?
- A) Generating frequent itemsets.
- B) Setting a minimum support threshold.
- C) Generating candidate itemsets.
- D) Calculating the support for itemsets.
Answer: C) Generating candidate itemsets.
Explanation: FP-Growth avoids candidate generation, which is a key step in the Apriori algorithm.
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) Sparse datasets with many frequent patterns.
- D) Small datasets with few transactions.
Answer: C) Sparse datasets with many frequent patterns.
Explanation: FP-Growth is efficient for sparse datasets where frequent patterns are present, as it compresses data effectively.
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.
Answer: B) Market basket analysis.
Explanation: FP-Growth is widely used in market basket analysis to identify frequent itemsets and association rules.
13. How is recursion used in the FP-Growth algorithm?
- A) To build the FP-Tree from transactions.
- B) To generate candidate itemsets for pruning.
- C) To mine patterns from conditional FP-Trees.
- D) To sort items based on frequency.
Answer: C) To mine patterns from conditional FP-Trees.
Explanation: Recursion is used to mine frequent patterns from conditional FP-Trees associated with each frequent item.
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.
Answer: B) It can handle datasets with continuous data directly.
Explanation: FP-Growth works with categorical data; continuous data needs to be discretized before applying the algorithm.
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) Descending order of their frequency in the dataset.
- D) Ascending order of their support values.
Answer: C) Descending order of their frequency in the dataset.
Explanation: Items are arranged in descending order of frequency to ensure efficient compression and traversal of the FP-Tree.