Apriori Algorithm

What is the Apriori algorithm used for in association rule mining?

• • To discover frequent itemsets from a transactional dataset

• To generate closed itemsets from a transactional dataset

• • To create maximal itemsets from a transactional dataset

• • To find association rules with the highest confidence in a transactional dataset

What is the key idea behind the Apriori algorithm for frequent itemset mining?

• • Using machine learning techniques to discover itemsets

• Employing an iterative approach to generate itemsets of increasing size based on the frequency of smaller itemsets

• Utilizing deep learning networks to uncover hidden patterns in the dataset

• Using statistical methods to calculate the significance of itemsets in the dataset

How does the Apriori algorithm handle the challenge of an exponentially growing number of possible itemsets?

• By using a brute-force approach to explore all possible itemsets

• By using pruning techniques to eliminate infrequent itemsets and reduce the search space

• By relying on external memory to store all possible itemsets for efficient exploration

• By setting a very high support threshold to limit the number of frequent itemsets

What is the main drawback of the Apriori algorithm in terms of its computational efficiency?

• • It is unable to discover frequent itemsets in large datasets

• It requires significant computational power to explore the search space of itemsets

• It can only handle binary transaction datasets

• It produces a large number of redundant frequent itemsets

What is the primary advantage of the Apriori algorithm in frequent itemset mining?

• It can efficiently find frequent itemsets in large transactional datasets

• It guarantees the discovery of all possible frequent itemsets in the dataset

• It can discover maximal itemsets more effectively than other algorithms

• It produces closed itemsets more accurately than other frequent itemset mining methods

What do minimum support and minimum confidence thresholds filter out in frequent pattern mining?

• Infrequent itemsets and rules

• Uninteresting itemsets and rules

• Noisy data

• Transaction reduction

How can the Apriori algorithm be optimized to improve its efficiency?

• Partitioning

• Sampling

• Transaction reduction

• All are correct

How does the hash-based technique contribute to improving the efficiency of the Apriori algorithm, particularly in the context of mining frequent itemsets?

• By directly eliminating infrequent itemsets during candidate generation

• By mapping candidate k-itemsets into buckets and reducing the size of the candidate set

• By employing dynamic counting to add candidate itemsets at various points during a scan

• By applying transaction reduction to reduce the number of transactions in subsequent iterations

What is a key characteristic of the sampling approach in the context of Apriori-based mining for frequent itemsets?

• It guarantees the discovery of all global frequent itemsets in a single pass

• It trades off some degree of accuracy for efficiency by mining on a subset of the data

• It uses a higher support threshold than the minimum support for local frequent itemsets

• It relies on partitioning the data into nonoverlapping segments