FP-growth algorithm MCQs

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.

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