ML-F-P Growth Algorithm

What is the main objective of the F-P Growth Algorithm?

• To build a decision tree

• To discover frequent itemsets in transactional databases

• To classify data into classes

• To perform clustering analysis

Which type of learning does the F-P Growth Algorithm belong to?

• Supervised learning

• Unsupervised learning

• Semi-supervised learning

• Reinforcement learning

What is the key data structure used in the F-P Growth Algorithm to represent the frequent itemsets?

• Linked list

• Stack

• F-P tree

• Hash table

Which property of the F-P Growth Algorithm allows it to efficiently mine frequent itemsets?

• Minimum Description Length

• Downward Closure Property

• Occam's Razor

• Maximum Likelihood Estimation

What is the primary advantage of the F-P Growth Algorithm over the Apriori Algorithm?

• It is more memory-efficient.

• It can handle categorical attributes.

• It guarantees a lower computational complexity.

• It can handle high-dimensional data.

The F-P Growth Algorithm is particularly efficient when dealing with datasets that have:

• Few transactions and few items per transaction

• Few transactions and many items per transaction

• Many transactions and few items per transaction

• Many transactions and many items per transaction

In the F-P Growth Algorithm, what is the purpose of the header table?

• To store the most frequent itemset

• To maintain the order of items in the F-P tree

• To store pointers to the first occurrence of each item in the F-P tree

• To keep track of the number of items in each transaction

What is the first step in the F-P Growth Algorithm?

• Building the F-P tree

• Scanning the dataset

• Pruning the F-P tree

• Generating candidate itemsets

Which approach does the F-P Growth Algorithm use to avoid generating candidate itemsets explicitly?

• Depth-First Search (DFS)

• Breadth-First Search (BFS)

• Top-Down

• Bottom-Up

The F-P Growth Algorithm recursively projects the F-P tree to mine frequent itemsets. What does "projecting" mean in this context?

• Generating a new dataset with fewer transactions

• Generating a new dataset with fewer items

• Removing infrequent itemsets from the F-P tree

• Building a new F-P tree with fewer branches